Data Science Lead

Manager Data Science & AI

Location: Chennai / Trivandrum

Experience: 10–15 years

Industry preference: FinTech / Banking / NBFC / Lending

Role Overview

We are looking for an experienced Data Science and AI Manager to lead, coach, and develop a team of data scientists while owning the delivery and business impact of statistical, machine-learning, and AI solutions. The role is accountable for setting priorities, allocating work, ensuring technical quality, and building a high-performing team aligned with organisational objectives.

The manager will partner with business and technology leaders to shape the analytics roadmap, translate strategic priorities into a governed portfolio of initiatives, and oversee solutions from discovery through deployment and monitoring. This includes managing delivery timelines and dependencies, establishing standards for model development and responsible AI, communicating risks and outcomes to senior stakeholders, and ensuring the team delivers measurable value in a FinTech, Banking, NBFC, or Lending environment.

Key Responsibilities

1. Data Analysis & Business Insights

• Oversee analytical work and ensure insights address priority business outcomes.
• Guide model design, validation, deployment, and lifecycle management.
• Review technical approaches, code quality, documentation, and production readiness.
• Own delivery commitments, resource allocation, dependencies, and escalation management.

2. Technical Leadership, Model Development & Lifecycle Management

• Set technical direction and guide the design, development, and validation of models for customer, risk, fraud, and business use cases.
• Ensure appropriate, explainable, and scalable methods, supported by robust feature engineering, evaluation, testing, and documentation.
• Enforce standards for production-quality Python and SQL, peer review, version control, and technical documentation.
• Oversee production deployment and monitoring for model accuracy, stability, drift, resilience, and business performance.
• Govern the model lifecycle—including approval, retraining, optimisation, versioning, and retirement—and coordinate with data engineering and technology teams to manage integration, dependencies, and service risks.

3. Collaboration with Business & Product Teams

• Prioritise the analytics and AI portfolio with business leaders.
• Define delivery roadmaps, milestones, dependencies, and success measures.
• Manage capacity across concurrent initiatives.
• Track benefits realisation and adoption after deployment.
• Manage and escalate delivery, data, technology, and compliance risks.

4. Advanced Analytics, Generative AI & Governance

• Evaluate and prioritise advanced analytics and Generative AI use cases based on business value, feasibility, suitability, and risk.
• Guide RAG, LLM, analytics assistant, and conversational data solutions, including architecture, evaluation, and the appropriate use of conventional analytics or machine learning.
• Establish standards for model governance, validation, approval, documentation, monitoring, audit readiness, and lifecycle controls.
• Ensure privacy, security, fairness, explainability, guardrails, human oversight, and regulatory compliance in partnership with Risk, Compliance, Information Security, Internal Audit, Legal, and Technology teams.

5. People Leadership & Team Management

• Lead, coach, and develop data scientists and AI professionals.
• Set objectives, allocate work, and manage performance and development plans.
• Build succession plans and support recruitment and onboarding.
• Promote collaboration, knowledge sharing, experimentation, and continuous learning.
• Balance hands-on technical guidance with effective delegation, accountability, and team empowerment.

Required Skills

Leadership, Delivery & Analytics

• Proven ability to lead, coach, and develop data science or AI teams, including performance and capability management.
• Experience managing concurrent analytics initiatives, priorities, capacity, dependencies, risks, and business outcomes.
• Strong stakeholder communication, influencing, and decision-making skills, with the ability to review and challenge technical solutions.
• Strong foundation in statistics, predictive modelling, experimentation, customer analytics, and translating business questions into clear analytical solutions.
• Experience managing vendors, platforms, budgets, or resource plans is desirable.

Technical & AI Capabilities

• Strong working knowledge of Python, SQL, statistics, machine learning, and modern model development practices, with the ability to guide and review team deliverables.
• Familiarity with cloud data and AI services—preferably AWS—and frameworks such as Scikit-learn, XGBoost, TensorFlow, PyTorch, MLflow, or equivalent tools.
• Ability to guide RAG and LLM solutions, establish appropriate controls, and select among conventional analytics, machine learning, and Generative AI approaches based on value and risk.

Qualifications & Leadership Experience

• Bachelor’s or master’s degree in a quantitative discipline, engineering, computer science, or a related field.
• 10–15 years of experience in analytics, data science, machine learning, or AI—preferably in FinTech, Banking, NBFC, or Lending—including 3–5 years of people or team leadership.
• Demonstrated success in delivering production analytics or AI solutions through adoption and measurable business impact.