Senior Data Scientist

Job Responsibilities

As Sr Data Scientist, you will be responsible for a wide range of engagements listed below:

• 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 Internal Use Only 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 (Must have)

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.

Pharma & RWE skillset

• 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

Non Tech

• Ability to recognize and pursue pragmatic alternatives vis--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. Internal Use Only
• 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.

Required Skills (Good to have)

Tech

• Advanced knowledge in one or more areas besides Machine Learning Operations Research, Natural Language Processing, Deep learning and its applications, Time Series forecasting at scale, Reinforcement Learning, Graph Machine Learning, Explainable Machine learning.
• Advanced understanding of Cloud technologies and experience of deploying applications on cloud.

Non Tech

• Hands-on experience working in the EMEA/Asia Pacific healthcare
• Ability to solution critical business problems into its component parts and match each such part with an appropriate technical approach.

Preferred Educational Qualifications :

• A bachelors degree in engineering, statistics, mathematics, computer science or another technical field.
• For those with a bachelors degree in non-technical fields, relevant prior work experience, technical aptitude, and coursework in data science from institutions of repute will be important