Data Science Manager - Pharma

Job Responsibilities:

As Manager-Delivery, 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.
• 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.
• Advanced knowledge of project management methodologies and tools
• Advanced knowledge levels in SQL and Python
• Intermediate to advanced knowledge in other areas of data science-namely, technical areas such as time series forecasting, Bayesian data analysis, Operations Research, and analytics domain areas such as Pricing analytics, Media Mix Modeling, B2B/B2C Customer analytics, etc.

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.
• Solution proposals, collaborating with growth, customer success, and central solutioning functions.

Being a Mathemagician:

• Understand & embody MathCo’s culture and way of working.
• Demonstrate an ownership mindset to drive results, striving for excellence.
• Engage actively and contribute to initiatives fostering company growth.
• Support diversity and understand different perspectives.

Preferred Educational Qualifications:

• An undergraduate degree in engineering, statistics, mathematics, computer science, or another technical field.
• For those with an undergraduate degree in non-technical fields, relevant prior work experience and technical aptitude will be important.

As a testament to our growth, we have been:

• Positioned as a Leader in Top Generative AI Service Providers - PeMa
• Quadrant 2023 by AIMResearch
• Recognized among the Inspiring Workplaces in North America, 2023
• Accredited for Inclusive Practices by Great Place to Work Institute, India
• Recognized among India’s Best Workplaces™ for Diversity, Equity, Inclusion Belonging 2023 by Great Place To Work® India