Senior Data Scientist

Key Responsibilities:

• Lead end-to-end ML/AI initiatives: problem definition (e.g., churn modelling, customer retention, risk analytics, dynamic process modelling), data exploration, feature engineering, model development, validation, deployment and monitoring in production.

• Design and build data pipelines and ML workflows leveraging Snowflake and Azure (data engineering, modelling, orchestration). Snowflake experiences are mandatory.

• Develop and deploy machine learning models at scale in cloud environments (Azure native services preferred).

• Apply agile frameworks and tools (Azure DevOps) to manage sprints, backlog, CI/CD, versioning, testing, and deployment of ML solutions.

• Collaborate with cross-functional teams (platform, data engineering, software development, business stakeholders, vendors) to ensure alignment with business objectives and delivery roadmap.

• Establish and promote best practices in MLOps, model governance, monitoring, versioning, retraining, and operationalization of AI solutions.

• Bring software development discipline into data science work: code reviews, modular design, unit/integration testing, documentation, UI/UX for ML tools.

• (Preferred) Build or influence user interfaces for ML/AI applications (for example using frameworks like Streamlit or notebooks/dashboards or using Databricks).

• Mentor junior data scientists and promote a culture of learning and innovation across the team.

• Communicate results clearly to both technical and non-technical stakeholders, translating data science outcomes into actionable business insights.

Key requirements (mandatory):

• Bachelor’s degree (or higher) in Computer Science, Physics, Statistics, Mathematics, Engineering or a related quantitative field.

• Minimum 4+ years of hands-on experience in machine learning/AI within industry — designing and deploying ML/AI projects.

• Strong practical expertise with Snowflake (data warehousing, Snowpark, stored procedures, dynamic tables etc.) this is mandatory.

• Proven experience on Azure cloud platform (Azure ML, Azure Data Factory, Azure Synapse etc.).

• Solid programming skills (Python, SQL) and software development experience (code versioning, testing, modular design).

• Experience in Agile practices for AI development.

• Experience working in Agile delivery frameworks and tools — particularly Azure DevOps (CI/CD pipelines, sprint planning, backlog management).

• Demonstrated track record of working on large scale ML projects: e.g., customer-retention/churn modelling, risk analytics, dynamic process modelling in an industrial/enterprise context.

• Understanding of MLOps lifecycle: deployment, monitoring, retraining, production support.

• Excellent communication skills and ability to work collaboratively across teams and geographies.