Job Description

Position Summary:

- Lead DS and ML predictive analytics solutions, owning technical strategy and delivery for data science projects of varying complexity.
- Serve as technical lead for data science squads — driving architecture decisions, code quality standards, and delivery excellence across the team.
- Bridge business and technical teams by translating complex analytical findings into transparent, actionable insights that influence strategic decisions.
- Foster a culture of continuous learning by mentoring data scientists, promoting best practices in MLOps, and championing innovation in analytical methods

Accountabilities:

Model Development & Analytics Leadership

- Build and deploy predictive models and analytics capabilities, leading technical workstreams independently
- Perform advanced data analysis and EDA to develop fact-based insights and strategic recommendations
- Lead integration of traditional and nontraditional data sources into production analytical workflows
- Drive integration of structured and unstructured data sources into robust, scalable analytical pipelines.

Platform and MLOps

- Architect and maintain ML infrastructure using cloud platforms (Databricks, AWS, Azure) and orchestration tools.
- Implement CI/CD practices for model deployment, versioning,



and automated retraining pipelines.
- Establish model monitoring, drift detection, and performance tracking frameworks in production.
- Promote reusable components, shared libraries, and standardised development environments across the team.

Leadership & Stakeholder Management

- Act as technical point of contact for stakeholders — translating business problems into clear data science problem statements.
- Lead cross-functional working groups with business owners, data engineers, and domain experts.
- Mentor and develop junior and mid-level data scientists through code reviews, knowledge sessions, and project coaching.
- Facilitate agile ceremonies (sprint planning, retrospectives, technical demos) and hold the team accountable to delivery commitments.

Strategic Contribution

- Identify and prioritise high-impact AI/ML opportunities across Commercial domains (Marketing, Sales, Pricing).
- Build and share domain expertise, contributing to the development of team capability roadmaps.
- Influence tooling and platform strategy decisions based on emerging best practices and business requirements.
- Drive measurable business outcomes — framing model outputs in terms of ROI, efficiency gains, or revenue impact.

📌 Data Scientist (Pune)
🏢 Syngenta Group
📍 Pune