Data Science Manager

Job Responsibilities:

As Manager-Data Science, you will be responsible for a wide range of

engagements listed below:

• Collaborate with the Engagement Manager, Account Delivery Manager, and client stakeholders to gather business requirements for the project.
• Develop a comprehensive project plan that aligns with the scope and objectives of the project.
• Develop appropriate solution design that will help client achieve their goals.
• Assign delivery team members to different activities suited to their individual skills.
• Lead the execution of project activities according to the project plan.
• Monitor the project, track milestones, and adhere to agreed-upon timelines and scope.
• Accountable for the delivery quality of the project in question.
• Comply with all the critical dimensions of the delivery scorecard, report as-is facts on those dimensions, and develop and execute action plans to improve delivery scores.
• Lead internal scrum meetings and stand-ups with the clients and Weekly Business Reviews with the clients.
• Ensure compliance with best practices and established processes for quality assurance—for example, using quality assurance checklists, coding best practices, peer reviews, and documentation.
• Provide both business and technical guidance to the delivery team.
• Coach individuals in the team and build a high-performance workplace.
• Establish an environment of mutual trust and respect and encourage the team to experiment with new delivery ideas.
• Review the team's deliverables before sharing them with clients, including codes, presentations, worksheets, emails, etc.

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-à-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 risk.