Data Science Manager

This role is for one of Weekday’s clients

Min Experience: 8+ years
Location: Pune, Maharashtra, India
JobType: full-time

We are looking for an experienced and results-oriented Data Science Manager to lead the delivery of data science and machine learning projects, with a focus on pharmaceutical and healthcare-related business challenges. The role involves collaborating with engagement managers, account delivery teams, and client stakeholders to understand business requirements, design analytical solutions, and ensure high-quality project execution.

The ideal candidate will combine strong expertise in probability, statistics, and practical machine learning with working knowledge of SQL, Python, and project management methodologies. They should be capable of translating business problems into effective data science solutions, guiding technical teams, managing client expectations, and ensuring projects are delivered within the agreed scope, timelines, and quality standards.

Key Responsibilities

1. Client Engagement and Requirement Gathering

• Collaborate with engagement managers, account delivery managers, and client stakeholders to understand business objectives, project requirements, and expected outcomes.
• Translate business requirements into well-defined analytical problems and data science project deliverables.
• Maintain effective communication with clients throughout the project lifecycle and ensure alignment on scope, milestones, and priorities.
• Lead client meetings, weekly business reviews, and project discussions to communicate progress, address challenges, and agree on next steps.

2. Data Science Solution Design

• Develop appropriate data science and machine learning approaches to address client business challenges.
• Apply advanced knowledge of probability and statistics to analyse data, validate assumptions, and generate meaningful insights.
• Design practical machine learning solutions while accounting for common pitfalls, model limitations, data quality issues, and potential sources of bias.
• Guide the selection of suitable analytical methodologies, modelling techniques, and evaluation metrics based on project objectives.
• Use SQL and Python to support data exploration, analysis, model development, and validation as required.

3. Project Planning and Delivery Management

• Develop comprehensive project plans covering scope, objectives, resource allocation, milestones, timelines, and deliverables.
• Allocate tasks to team members based on their skills, experience, and project requirements.
• Lead project execution and monitor progress against agreed plans, identifying risks and implementing corrective actions where necessary.
• Track project milestones, manage dependencies, and ensure timely delivery without compromising quality.
• Apply appropriate project management methodologies and tools to improve delivery efficiency and accountability.

4. Quality Assurance and Performance Monitoring

• Take ownership of project delivery quality and ensure all deliverables meet established standards and client expectations.
• Monitor delivery performance across key metrics and scorecard dimensions, report progress accurately, and develop action plans to address gaps.
• Establish and enforce quality assurance practices, including peer reviews, documentation standards, coding best practices, and quality checklists.
• Review analytical outputs, code, presentations, reports, spreadsheets, and client communications before delivery.
• Identify opportunities to improve project processes, analytical quality, and overall delivery performance.

5. Team Leadership and Development

• Provide technical and business guidance to data scientists, analysts, and other delivery team members.
• Lead internal scrum meetings, daily stand-ups, and project planning discussions to maintain alignment and execution discipline.
• Coach team members, support their professional development, and encourage continuous learning.
• Foster a high-performance work environment built on collaboration, accountability, mutual trust, and respect.
• Encourage experimentation, innovation, and the adoption of improved data science and project delivery practices.

6. Pharmaceutical Domain Application

• Apply data science methodologies to business problems within the pharmaceutical and healthcare ecosystem.
• Understand client objectives and translate domain-specific requirements into relevant analytical approaches.
• Support the development of data-driven recommendations that improve business decision-making and project outcomes.

Must-Have Skills and Qualifications

• Pharmaceutical Domain: Relevant exposure to the pharmaceutical industry or pharma-related data science projects.
• Probability and Statistics: Advanced knowledge of statistical methods, probability theory, and analytical techniques.
• Machine Learning: Advanced understanding of practical machine learning methods, model evaluation, common pitfalls, and mitigation approaches.
• SQL and Python: Intermediate proficiency in SQL and Python for data analysis, querying, and analytical problem-solving.
• Project Management: Intermediate knowledge of project management methodologies, planning tools, milestone tracking, and delivery processes.
• Client and Stakeholder Management: Ability to gather requirements, communicate progress, manage expectations, and maintain strong client relationships.
• Leadership: Experience guiding team members, reviewing deliverables, and maintaining high standards of execution.
• MS Office: Intermediate proficiency in Microsoft Excel, PowerPoint, and Word for analysis, reporting, and presentations.
• Quality and Delivery Management: Ability to monitor project performance, identify delivery risks, and implement quality improvement initiatives.