Pharmaceutical professionals believe artificial intelligence (AI)will be the most disruptive technology in the industry in 2021. As AI and machine learning (ML) become crucial tools for keeping pace in the industry, clinical development is an area that can substantially benefit, delivering significant time and cost efficiencies while providing better, faster insights to inform decision making. However, for patients, these tools provide improved safety practices that lead to better, safer, drugs. Here is how AI/ML can be used to support pharma companies in delivering safer drugs to market.

Overcoming Barriers to Using AI in Clinical Research

Today, AI and ML can be used to support clinical research in numerous ways; including the identification of molecules that hold potential for clinical treatments, finding patient populations that meet specific criteria for inclusion or exclusion, as well as analyzing scans, claims reports, and other healthcare data to identify trends in clinical research and treatments that lead to safer and faster decisions.

However, to take full advantage of the benefits of AI/ML technology, organizations performing clinical trials must first gain access to the tools, expertise, and industry-specific datasets enabling them to build algorithms to fit their specific needs. Healthcare data, unlike purely numerical data pulled from monitoring systems and tools such as IoT or SaaS platforms, is typically unstructured due to the way the data is collected (through doctor visits, and unstructured web sources) and must meet strict security protocols to ensure patient privacy.

To truly leverage AI and ML for clinical research, data must be collected, studied, combined, and protected to make effective healthcare decisions. When clinical researchers collaborate with partners that have both technical and pharmaceutical expertise, they ensure that data is being structured and analyzed in a way that simultaneously reduces risks and improves the quality of clinical research.

The Benefits of AI for Clinical Research

When it comes to research study design, site identification and patient recruitment, and clinical monitoring, AI and ML hold great potential to make clinical trials faster, more efficient, and most importantly: safer.

Study design sets the stage for a clinical research initiative. The cost, efficiency, and potential success of clinical trials rest squarely on the shoulders of the study’s design and plans. AI and ML tools, along with natural language processing (NLP), can analyze large sets of healthcare data to assess and identify primary and secondary endpoints in clinical research design. This ensures that protocols for regulators, payers, and patients are well defined before clinical trials commence. Defining parameters such as these optimize study design by helping to identify ideal research sites and enrollment models. Ultimately, better study design leads to more predictable results, reduced cycle time for protocol development, and a generally more efficient study.

Identifying trials sites and recruiting patients for clinical research is a tougher task than it seems to be at face value. Clinical researchers must identify the area that will provide enough access to patients who meet inclusion and exclusion criteria. As studies become more focused on rarer conditions or specific populations, recruiting participants for clinical trials becomes more difficult, which increases the cost, timeline, and risk of failure for the clinical study if enough patients cannot be recruited for the research. AI and ML tools can support site identification for clinical research by mapping patient populations and proactively targeting sites with the most potential patients that meet inclusion criteria. This enables fewer research sites to meet recruitment requirements and reduce the overall cost of patient recruitment.

Clinical monitoring is a tedious manual process of analyzing site risks of clinical research and determining specific actions to take towards mitigating those risks. Risks in clinical research include recruitment or performance issues, as well as risks to patient safety. AI and ML automate the assessment of risks in the clinical research environment, and provide suggestions based on predictive analytics to better monitor for and prevent risks. Automating this assessment removes the risk of manual error, and decreases the time spent on analyzing clinical research data.

Strategies for Using AI for Clinical Research

During clinical trials, there’s a limited patient population to pull from, as research subjects must meet pre-set parameters for inclusion in the study. On the other hand, as opposed to post-market research, clinical researchers are blessed with vast amounts of information surrounding their patients including what drugs they are taking, their health history, and their current environment.

In addition, because the clinical researcher is working closely with the patient and is well-educated on the drug or product being researched, the researcher is very familiar with all potential variables involved in the clinical trial. To put it simply, clinical trials have a lot of information to analyze, but few patients with whom to conduct the research. Because of this disproportionate ratio of information over patients, every case in a clinical research setting is extremely important to the future of the drug being researched.

The massive amount of patient and drug information available to clinical researchers necessitates the use of NLP tools to analyze and process documents and patient records.NLP can search documents and records for specific terms, phrases, and words that might indicate a problem or risk in the clinical trial. This eliminates the need for manual analysis of clinical trial data – reducing, and in some cases eliminating, the risk of human error while also increasing patient safety. This is especially useful in lengthy clinical trials, for which researchers will need to analyze patient histories and drug results over an extended period of time. Many clinical trials have long document trails and questionnaires that can add up to hundreds of pages of patient data that researchers must analyze.

In a clinical trial, researchers are ultimately trying to determine whether the benefits of a specific treatment outweigh the risks. AI can be especially helpful in clinical trials of high-risk drugs. If a researcher knows that a drug cures or alleviates an illness or condition, but also know that the potential side effects of that drug can have a significant negative impact on the patient, they’ll want to know how to determine if a patient is likely to present those negative side effects. NLP can be used to produce word clouds of potential signals of the negative side effects of a drug that patients would experience.

The only way to do this type of analysis manually is to identify those words using human researchers, then analyze the patient reports to find those words, and group those reports into risk profiles. NLP can automate that entire process and provide insights on risk indicators in patients much more efficiently and safely than human researchers ever could.

Integrating AI & ML with Clinical Research Creates Competitive Results

AI and ML technologies, especially NLP, hold huge promise to support and optimize clinical research. However, that assurance can only be achieved by organizations that have the necessary tools, expertise, and partners to leverage the full benefits of AI and ML. AI and ML solutions support the optimization of clinical research by more efficiently analyzing research data for risks and allowing faster trial planning and research. Those who fail to engage AI and ML for clinical research may find that their competitors are doing so, and as a result, are going to market with new drugs and products faster with higher profits due to decreased research time and safer practices.

Author

Updesh Dosanjh, Practice Leader, Pharmacovigilance Technology Solutions, IQVIA

As Practice Leader for the Technology Solutions business unit of IQVIA, Updesh Dosanjh is responsible for developing the overarching strategy regarding Artificial Intelligence and Machine Learning as it relates to safety and pharmacovigilance. He is focused on the adoption of these innovative technologies and processes that will help optimize pharmacovigilance activities for better, faster results.  Dosanjh has over 25 years of knowledge and experience in the management, development, implementation, and operation of processes and systems within the life sciences and other industries.  Most recently, Dosanjh was with Foresight and joined IQVIA as a result of an acquisition. Over the course of his career, Dosanjh also worked with WCI, Logistics Consulting Partners, Amersys Systems Limited, and FJ Systems. Dosanjh holds a Bachelor’s degree in Materials Science from Manchester University and a Master’s degree in Advanced Manufacturing Systems and Technology from Liverpool University.

The post SEIZING THE OPPORTUNITY TO LEVERAGE AI & ML FOR CLINICAL RESEARCH appeared first on Artificial Intelligence.

AI and data science jobs are already seen as a rewarding career path for professionals

Artificial intelligence is a promising technology, that has made significant changes in the 21st century. Starting from self-driving cars and robotic assistants to automated disease diagnosis and drug discovery, the stronghold of artificial intelligence is no joke. Along with artificial intelligence, data science has also shifted the way we live and work. With the demand for data science and artificial intelligence spiralling, the job market is opening its door to AI and data science jobs. The tech sphere has ensured that artificial intelligence jobs and data science jobs provide limitless opportunities for professionals to explore cutting edge solutions. According to a Gartner report, Artificial intelligence jobs rose to over 2.3 million in 2020. While the competition in the industry is heating up, AI and Data science jobs are already seen as the rewarding career path. Analytics Insight has listed top AI and Data science jobs that aspirants should apply for today.

• LATEST PLACEMENT ALERT: TOP HIGHEST PAYING TECH JOBS TO APPLY IN 2021
• DATA SCIENCE JOBS ALERT: TOP OPENINGS TO APPLY FOR THIS WEEK
• PLACEMENT ALERT: TOP DATA ANALYTICS JOBS OF THE YEAR 2021 IN INDIA

Data Scientist: Artificial Intelligence at IBM–

Location: Bengaluru, Karnataka, India

About the company: IBM, also known as International Business Machines Corporation is a leading American computer manufacturer. The company has developed a thoughtful, comprehensive approach to corporate citizenship that they believe aligns with IBM’s values and maximized the impact they can make as a global enterprise.

Roles and responsibilities: As a data scientist at IBM, the candidate is expected to develop, maintain, and evaluate AI solutions. He/she will be involved in the design of data solutions using artificial intelligence-based technologies like H2O, Tensorflow. They are responsible for designing algorithms and implementation including loading from disparate datasets, pre-processing using Hive and Pig. The candidate should scope and deliver solutions with the ability to design solutions independently based on high-level architecture. They should also maintain the production systems like Kafta, Hadoop, Cassandra, and Elasticsearch.

Qualifications

• The candidate should have a minimum of 4 years of experience in the IT industry.
• They should be technologically wise in Python programming and have proficient skills in Watson NLP.
• He/she should know to develop and embed AI solutions in Intelligent Workflow.
• The candidate should have working knowledge in one NoSQL database like MongoDB, Cassandra, HBase, or Couchbase.
• They should have hands-on experience in big data architectures.

Senior Analyst- Artificial Intelligence Innovation at Accenture–

Location: Bengaluru, Karnataka, India

About the company: Accenture is a global professional services company that provides a range of services and solutions in strategy, consulting, digital, technology, and operations. Combining deep experience and specialized skills across 40 industries and business functions, Accenture works at the intersection of business and technology to help clients improve performance and create sustainable value for stakeholders.

Roles and responsibilities: As a senior analyst- artificial intelligence innovation, the candidate will be aligned with Accenture’s insights and intelligence vertical and help them generate insights by leveraging the latest artificial intelligence and analytics techniques to deliver value to its clients. Generally, the artificial intelligence innovation team at Accenture is responsible for the creation, deployment, and managing of the operations of projects. In this role, the candidate will need to analyze and solve increasingly complex problems. He/she should frequently interact with their peers at Accenture and clients to manage the development well.

Qualifications

• The candidate is mandated to own a B.E/B.Tech/M.Tech degree.
• He/she should have at least 5-8 years of experience in working with artificial intelligence.

Principal, Artificial Intelligence Engineer at AT&T–

Location: Azcapotzalco, Mexico City, Mexico

About the company: AT&T is a US-based telecom company and the second largest provider of mobile services. AT&T operates as a carrier of both fixed and mobile networks in the US but offers telecoms services elsewhere. The company also provides pay-TV services through DirecTV.

Roles and responsibilities: The artificial intelligence engineer at AT&T is responsible for designing and implementing artificial intelligence and machine learning packages, including data pipelines, to process complex, large-scale datasets used for modelling, data mining, and research purposes. He/she is expected to design, develop, troubleshoot, debug, and modify software for AT&T services or the management and monitoring of these service offerings. They should interact with systems engineers t realize the technical design and requirements of the service, including management, systems, and data aspects.

Qualifications

• The candidate is expected to have a Bachelors degree in Computer Science, or Scientific Computing, Data Analytics, Machine Learning, Business Analyst nanodegree, or any equivalent experience.
• He/she should have experience working with Java, Python, SQL, Azure, etc.

Data Engineer at LinkedIn–

Location: Bengaluru, Karnataka, India

About the company: LinkedIn is a social networking site designed to help people make business connections, share their experience and resumes, and find jobs. LinkedIn is free, but a subscription version called LinkedIn Premium offers additional features like online classes and seminars.

Roles and responsibilities: As a data engineer at LinkedIn, the candidate is expected to work with a team of high-performing analytics, data science professionals, and cross-functional teams to identify business opportunities, optimize product performance or go to market strategy. He/she should build data expertise and manage complex data systems for a product or a group of products. They should perform all the necessary data transformation tasks to serve products that empower data-driven decision-making. The candidate should establish efficient design and programming patterns for engineers as well as for non-technical partners.

Qualifications

• The candidate is mandated to have Bachelors in a quantitative discipline: statistics, operations research, computer science, informatics, engineering, applied mathematics, economics, etc.
• He/she should have 3+ years of experience working with a large number of datasets.
• They should have hands-on experience in SQL and relational databases.
• The candidate should have experience with distributed data systems such as Hadoop and related technologies like Spark, Presto, Pig, Hive, etc.

Data Scientist, Engineering at Google–

Location: Bengaluru, Karnataka, India

About the company: Google is an American search engine company founded in 1998. Began as an online search firm, the company now offers more than 50 internet services and products including email, online document creation, software for mobile phones and tablet computers, etc.

Roles and responsibilities: As a data scientist at Google, the candidate will evaluate and improve Google’s products. They will collaborate with a multi-disciplinary team of Engineers and Analysts on a wide range of problems, bringing analytical rigour and statistical methods to the challenges of measuring quality, improving consumer products, and understanding the behaviour of end-users, advertisers, and publishers. He/she should work with large, complex data sets and solve difficult, non-routine analysis problems by applying advanced methods. The candidate should conduct end-to-end analysis, including data gathering and requirements specification, processing, analysis, ongoing deliverables, and presentations.

Qualifications

• The candidate should have a Master’s degree in a quantitative discipline or equivalent practical experience.
• He/she should have at least 2 years of experience in data analysis or a related field.
• They should be well-versed in statistical software.

Data Engineer at Tata Consultancy Services-

Location: Melbourne, Victoria, Australia

About the company: Tata Consultancy Services, also known as TCS, is a global leader in IT services, digital, and business solutions. The company partners with clients to simplify, strengthen, and transform their business.

Roles and responsibilities: As a data engineer, the candidate should design and build production data pipelines from ingestion to consumption within a big data architecture, using Java, Python, or Scala. He/she should design and implement data engineering, ingestion, and curation functions on AWS cloud using AWS native or custom programming.

Qualifications

• The candidate is expected to have 4+ years of experience working with AWS cloud, Big data technologies, AWS Cloud Data Services like RedShift, Lambda, Glue, etc.
• He/she should have the experience to build, design, and operationalize enterprise data solutions and applications using one or more AWS data and analytics services.
• They should have experience in ETL Tools, data modelling, data analysis, Python, Shell scripting, ETL Testing, Splunk tools, etc.

The post GRAB THE OPPORTUNITY: TOP AI AND DATA SCIENCE JOBS TO APPLY TODAY appeared first on Artificial Intelligence.

AI and data science jobs are already seen as a rewarding career path for professionals

Artificial intelligence is a promising technology, that has made significant changes in the 21st century. Starting from self-driving cars and robotic assistants to automated disease diagnosis and drug discovery, the stronghold of artificial intelligence is no joke. Along with artificial intelligence, data science has also shifted the way we live and work. With the demand for data science and artificial intelligence spiralling, the job market is opening its door to AI and data science jobs. The tech sphere has ensured that artificial intelligence jobs and data science jobs provide limitless opportunities for professionals to explore cutting edge solutions. According to a Gartner report, Artificial intelligence jobs rose to over 2.3 million in 2020. While the competition in the industry is heating up, AI and Data science jobs are already seen as the rewarding career path. Analytics Insight has listed top AI and Data science jobs that aspirants should apply for today.

• LATEST PLACEMENT ALERT: TOP HIGHEST PAYING TECH JOBS TO APPLY IN 2021
• DATA SCIENCE JOBS ALERT: TOP OPENINGS TO APPLY FOR THIS WEEK
• PLACEMENT ALERT: TOP DATA ANALYTICS JOBS OF THE YEAR 2021 IN INDIA

Data Scientist: Artificial Intelligence at IBM–

Location: Bengaluru, Karnataka, India

About the company: IBM, also known as International Business Machines Corporation is a leading American computer manufacturer. The company has developed a thoughtful, comprehensive approach to corporate citizenship that they believe aligns with IBM’s values and maximized the impact they can make as a global enterprise.

Roles and responsibilities: As a data scientist at IBM, the candidate is expected to develop, maintain, and evaluate AI solutions. He/she will be involved in the design of data solutions using artificial intelligence-based technologies like H2O, Tensorflow. They are responsible for designing algorithms and implementation including loading from disparate datasets, pre-processing using Hive and Pig. The candidate should scope and deliver solutions with the ability to design solutions independently based on high-level architecture. They should also maintain the production systems like Kafta, Hadoop, Cassandra, and Elasticsearch.

Qualifications

• The candidate should have a minimum of 4 years of experience in the IT industry.
• They should be technologically wise in Python programming and have proficient skills in Watson NLP.
• He/she should know to develop and embed AI solutions in Intelligent Workflow.
• The candidate should have working knowledge in one NoSQL database like MongoDB, Cassandra, HBase, or Couchbase.
• They should have hands-on experience in big data architectures.

Senior Analyst- Artificial Intelligence Innovation at Accenture–

Location: Bengaluru, Karnataka, India

About the company: Accenture is a global professional services company that provides a range of services and solutions in strategy, consulting, digital, technology, and operations. Combining deep experience and specialized skills across 40 industries and business functions, Accenture works at the intersection of business and technology to help clients improve performance and create sustainable value for stakeholders.

Roles and responsibilities: As a senior analyst- artificial intelligence innovation, the candidate will be aligned with Accenture’s insights and intelligence vertical and help them generate insights by leveraging the latest artificial intelligence and analytics techniques to deliver value to its clients. Generally, the artificial intelligence innovation team at Accenture is responsible for the creation, deployment, and managing of the operations of projects. In this role, the candidate will need to analyze and solve increasingly complex problems. He/she should frequently interact with their peers at Accenture and clients to manage the development well.

Qualifications

• The candidate is mandated to own a B.E/B.Tech/M.Tech degree.
• He/she should have at least 5-8 years of experience in working with artificial intelligence.

Principal, Artificial Intelligence Engineer at AT&T–

Location: Azcapotzalco, Mexico City, Mexico

About the company: AT&T is a US-based telecom company and the second largest provider of mobile services. AT&T operates as a carrier of both fixed and mobile networks in the US but offers telecoms services elsewhere. The company also provides pay-TV services through DirecTV.

Roles and responsibilities: The artificial intelligence engineer at AT&T is responsible for designing and implementing artificial intelligence and machine learning packages, including data pipelines, to process complex, large-scale datasets used for modelling, data mining, and research purposes. He/she is expected to design, develop, troubleshoot, debug, and modify software for AT&T services or the management and monitoring of these service offerings. They should interact with systems engineers t realize the technical design and requirements of the service, including management, systems, and data aspects.

Qualifications

• The candidate is expected to have a Bachelors degree in Computer Science, or Scientific Computing, Data Analytics, Machine Learning, Business Analyst nanodegree, or any equivalent experience.
• He/she should have experience working with Java, Python, SQL, Azure, etc.

Data Engineer at LinkedIn–

Location: Bengaluru, Karnataka, India

About the company: LinkedIn is a social networking site designed to help people make business connections, share their experience and resumes, and find jobs. LinkedIn is free, but a subscription version called LinkedIn Premium offers additional features like online classes and seminars.

Roles and responsibilities: As a data engineer at LinkedIn, the candidate is expected to work with a team of high-performing analytics, data science professionals, and cross-functional teams to identify business opportunities, optimize product performance or go to market strategy. He/she should build data expertise and manage complex data systems for a product or a group of products. They should perform all the necessary data transformation tasks to serve products that empower data-driven decision-making. The candidate should establish efficient design and programming patterns for engineers as well as for non-technical partners.

Qualifications

• The candidate is mandated to have Bachelors in a quantitative discipline: statistics, operations research, computer science, informatics, engineering, applied mathematics, economics, etc.
• He/she should have 3+ years of experience working with a large number of datasets.
• They should have hands-on experience in SQL and relational databases.
• The candidate should have experience with distributed data systems such as Hadoop and related technologies like Spark, Presto, Pig, Hive, etc.

Data Scientist, Engineering at Google–

Location: Bengaluru, Karnataka, India

About the company: Google is an American search engine company founded in 1998. Began as an online search firm, the company now offers more than 50 internet services and products including email, online document creation, software for mobile phones and tablet computers, etc.

Roles and responsibilities: As a data scientist at Google, the candidate will evaluate and improve Google’s products. They will collaborate with a multi-disciplinary team of Engineers and Analysts on a wide range of problems, bringing analytical rigour and statistical methods to the challenges of measuring quality, improving consumer products, and understanding the behaviour of end-users, advertisers, and publishers. He/she should work with large, complex data sets and solve difficult, non-routine analysis problems by applying advanced methods. The candidate should conduct end-to-end analysis, including data gathering and requirements specification, processing, analysis, ongoing deliverables, and presentations.

Qualifications

• The candidate should have a Master’s degree in a quantitative discipline or equivalent practical experience.
• He/she should have at least 2 years of experience in data analysis or a related field.
• They should be well-versed in statistical software.

Data Engineer at Tata Consultancy Services-

Location: Melbourne, Victoria, Australia

About the company: Tata Consultancy Services, also known as TCS, is a global leader in IT services, digital, and business solutions. The company partners with clients to simplify, strengthen, and transform their business.

Roles and responsibilities: As a data engineer, the candidate should design and build production data pipelines from ingestion to consumption within a big data architecture, using Java, Python, or Scala. He/she should design and implement data engineering, ingestion, and curation functions on AWS cloud using AWS native or custom programming.

Qualifications

• The candidate is expected to have 4+ years of experience working with AWS cloud, Big data technologies, AWS Cloud Data Services like RedShift, Lambda, Glue, etc.
• He/she should have the experience to build, design, and operationalize enterprise data solutions and applications using one or more AWS data and analytics services.
• They should have experience in ETL Tools, data modelling, data analysis, Python, Shell scripting, ETL Testing, Splunk tools, etc.

The post GRAB THE OPPORTUNITY: TOP AI AND DATA SCIENCE JOBS TO APPLY TODAY appeared first on Artificial Intelligence.

Over the last decade, data science has been rapidly progressing both as a technology and as a discipline. Best practices have been created by the leading businesses and it is now becoming part of the operational core for organizations. However, there is a need for a next step for product evolution in data science platform that supports and provides both business users an integrated solution for managing, building, and optimizing predictive models.

Nowadays, data science platform is the most talked about topic in data science meet-ups, conferences, and top publications. According to a Research Dive analyst review, the concept of data science platform is not novel in the big data space but the need of data science platform in business is still unknown to many.

Need for a Data Science Platform

 1)    To Enable Better Teamwork with Data Scientists

If the data scientists are solving the same problem in several ways, the productivity will decrease as it won’t deliver effectual value to the organization. One of the best solutions to ensure effective teamwork with data scientists is to provide them with a centralized flexible platform and the required set of tools to work upon. By using a data science platform, it ensures that all the contributions of the data scientists i.e. data models, data visualizations, and code libraries exist in a single shared reachable location. This helps data scientists to reuse the code, facilitate better discussion around research projects, and share best practices to make data science easily scalable and less resource exhaustive.

2)    Help Minimalize Engineering Effort

With data science platforms, the data scientists get help in moving analytical models into production without any need of additional engineering effort or DevOps. For instance, if a company wants to build a product recommendation engine then the data scientist will require the efforts of a software engineer for testing, refining and integrating the data model before the users start seeing the product recommendations on the basis of their behavior. A data science platform makes sure that the data models are accessible behind an API so that the data scientists do not have to depend much on engineering efforts.

3)    Help to Offload a Number of Low Value Tasks

The burden of data scientists is released with the help of data science platforms. The burden of low value tasks such as reproducing past results, configuring environments for non-technical users, running reports, and scheduling jobs is offloaded from data scientists.

4)    Facilitate Faster Research and Experimentation

Data scientists do not have to deal with extra data management tasks, as data science platforms allow people to see what and how others are working on. Moreover, whenever there is a new hire in the data science team, the employee can quickly start working as it is easier to restore the work of the people who leave through a unified platform over various isolated tools.

The Market Overview

Currently, the global market for data science platform is progressing rapidly and is about to positively grow in the near future. According to the Research Dive report, the global data science platform market is projected to garner a revenue of $224.3 billion at a 31.1% CAGR from 2019 to 2026. This is majorly due to the growing adoption of analytical tools across the globe for learning the unobserved customer purchasing pattern. The key prominent players of the market are adopting several strategies such as product development along with many approaches such as collaborations and R&D activities to stand strong in the global market. The major players of the global data science market include Alphabet Inc. (Google), Databricks, Domino Data Lab, Inc., Civis Analytics, Dataiku, Cloudera, Inc., IBM Corporation, Anaconda, Inc., Microsoft Corporation., and Altair Engineering, Inc.

The post Data Science Platform Market: created an opportunity to transform in various sectors appeared first on Artificial Intelligence.

Developing data science skills is one of the best things that you can do for your career. Not only does data science open global career opportunities to you – it’s a skill that businesses use in every field across the world – but it also equips you with the capabilities that boards and CEOs will be looking for when recruiting executives. In other words, for young and ambitious professionals, it’s a skill that will help you elevate your career quickly.

Where’s the demand for data scientists, and why?

One of the reasons why data scientists are flourishing in today’s job market is because their skills are transferable just about everywhere, across all sectors and industries. City planners are gathering reams of data to build ‘smart cities’ that can cope with increased population densities. Retail has been up-ended because Amazon has so effectively leveraged data science to improve everything from logistics to marketing. Putting aside the controversies over the Australian government’s My Health Record, patient data is becoming the driving force of healthcare, and that sector is desperately recruiting data scientists to properly analyse and manage the data.

To give you an idea of just how varied data science can be in application, the Towards Data Science blog published a number of potential use cases for data science that are as varied as they are fascinating in the potential they offer their respective sectors:

• Predicting the best retail location for a store
• Predicting why patients are being readmitted
• Detecting insurance fraud
• Predicting product needs and prices live as a consumer walks into a brick-and-mortar store
• Identifying who to call for fundraisers
• Predicting when a patient needs behavioural health procedures partnered with their physical medical procedures

That’s just scratching the surface. Data scientists are in such demand because they transform and modernise whatever organisation they work for. They are in demand everywhere in the world, just as technology is impacting on how business is done everywhere in the world.

Developing data science skills is one of the best things that you can do for your career. Not only does data science open global career opportunities to you – it’s a skill that businesses use in every field across the world – but it also equips you with the capabilities that boards and CEOs will be looking for when recruiting executives. In other words, for young and ambitious professionals, it’s a skill that will help you elevate your career quickly.

Where’s the demand for data scientists, and why?

One of the reasons why data scientists are flourishing in today’s job market is because their skills are transferable just about everywhere, across all sectors and industries. City planners are gathering reams of data to build ‘smart cities’ that can cope with increased population densities. Retail has been up-ended because Amazon has so effectively leveraged data science to improve everything from logistics to marketing. Putting aside the controversies over the Australian government’s My Health Record, patient data is becoming the driving force of healthcare, and that sector is desperately recruiting data scientists to properly analyse and manage the data.

To give you an idea of just how varied data science can be in the application, the Towards Data Science blog published a number of potential use cases for data science that are as varied as they are fascinating in the potential they offer their respective sectors:

• Predicting the best retail location for a store
• Predicting why patients are being readmitted
• Detecting insurance fraud
• Predicting product needs and prices live as a consumer walks into a brick-and-mortar store
• Identifying who to call for fundraisers
• Predicting when a patient needs behavioural health procedures partnered with their physical medical procedures

That’s just scratching the surface. Data scientists are in such demand because they transform and modernise whatever organisation they work for. They are in demand everywhere in the world, just as technology is impacting on how business is done everywhere in the world.

The post Data science: the career path for professionals that want options appeared first on Artificial Intelligence.