Senior Data Scientist

Role Overview:

- Formulates complex problems into hypotheses and proofs of concept for testing. Socializes this with the rest of the team and guides them to deliver on it.

- Applies business acumen in analyses to extract meaningful and actionable information.

- Communicates complex ideas and insights effectively to stakeholders with various levels of technical experience.

- Leverages various analytical techniques, appropriate to solving the business problem at hand.

- Performs research for solutions and solves problems efficiently to overcome data analytics challenges.

- Engages in the collection, integration, analysis, and presentation of data for various business contexts.

- Understands good software development practices (versioning, peer reviews, refactoring, and pair programming), adapts them to the needs of the team and sets an example for others by following them.

- Creates and maintains technical documentation of the scientific experimentations performed, preferably in the CRISP-DM framework or like.

- Keeps up with the latest trends, technologies, and best practices in the field of Data Science and Engineering. Shares this knowledge with the rest of the data science team.

- Does peer reviews of codes and approaches of fellow members of the data science team.

- Provides mentorship to junior data scientists in the team.

- Looks for improvements in data science delivery processes, actively seeks buy-in on the improvements from peers and managers, implements them in the team and monitors their adoption.

Required Skills (Tech):

- Advanced programming knowledge in Python and SQL.

- Advanced knowledge in Probability and Statistics, including hypothesis testing.

- Advanced knowledge in Practical Machine Learning and awareness of the key-pitfalls in the practice of machine learning and approaches to addressing them.

- Knowledge of data visualization technologies like Tableau, and PowerBI, and comfortable using relevant libraries in Python like seaborn and matplotlib.

- Experienced in modern development tools and writing code collaboratively.

- Intermediate knowledge of Cloud technologies and experience in developing data science solutions in one or more cloud platforms.

Required Skills (Pharma & RWE):

- Experience working with patient level data such as hands-on experience with RWE datasets such as Optum, Komodo, IQVIA Claims in depth.

- Experience working on Real World Evidence Use cases such as patient journey, Identifying Adherence, Drop-offs, Productivity and Days on therapy and Survival analysis Sequence model for.

- Ability to understand and solution for Pharmaceutical and Lifesciences analytical and business solutions.

Required Skills (Non-Tech):

- Ability to recognize and pursue pragmatic alternatives vis-a-vis a perfect solution, balancing priorities of time with potential business impact.

- Plan projects, break them down across individual data scientists in the team, track their performance and manage risks.

- Ability to storyboard an entire presentation to a non-technical audience.

- Ability to work independently to develop data science solutions, while also being able to work as part of a team to communicate findings and collaborate on solutions.

- Strong written skills. This is required for submitting technical papers, whitepapers, and developing project documentations.

- Technical leadership and mentorship to the community of data scientists in the organization.

Preferred Educational Qualifications:

- A bachelor's degree in engineering, statistics, mathematics, computer science or another technical field.

- For those with a bachelor's degree in non-technical fields, relevant prior work experience, technical aptitude, and coursework in data science from institutions of repute will be important.