Sr Data Scientist

Overview

The Sr Data Scientist will translate ambiguous business and product asks into AI/ML use cases and deliver business-impacting data, predictive, optimization, and GenAI-enabled solutions for IoT-oriented applications.

What you'll do

• Partner with product and business stakeholders to translate ambiguous asks into clear AI/ML use cases.
• Own product relationships for assigned use cases by managing expectations, surfacing risks early, and aligning stakeholders around tradeoffs and business outcomes.
• Lead exploratory analysis, feature engineering, model selection, experiment design, and statistical validation for time series, forecasting, anomaly detection, and other IoT-oriented use cases.
• Work with IoT and sensor-based data, including irregular intervals, missingness, and event-driven signals.
• Define strong baseline approaches and recommend the simplest effective solution.
• Build and evaluate predictive, optimization, and GenAI-enabled solutions using reproducible workflows in Databricks.
• Help define and deliver data products that are reusable, maintainable, and valuable to downstream users, systems, or business processes.
• Use GitHub and Azure DevOps with strong version control, pull request discipline, documentation, and work tracking practices.
• Contribute to API-oriented solution design by shaping model inputs/outputs, integration expectations, and consumption patterns for downstream applications.
• Mentor junior and mid-level data scientists and contribute reusable templates and team standards.

What you'll need

• 6+ years of experience in data science, machine learning, or applied AI with a track record of delivering business-impacting solutions.
• Strong programming skills in Python, PySpark, and SQL.
• Solid grounding in statistics, machine learning, experimentation, and model evaluation.
• Hands-on experience with Databricks for exploratory analysis, model development, and reproducible ML workflows.
• Demonstrated experience with time series modeling, forecasting, anomaly detection, and/or sequential data problems.
• Experience working with IoT, sensor, telemetry, or other operational data sources.
• Strong data engineering capability, including data wrangling at scale, feature pipeline design, dataset preparation, data quality troubleshooting, and support for production-ready analytical workflows.
• Experience creating data products or analytics products intended for repeated use.
• Experience designing baselines, features, evaluation frameworks, and error analysis approaches for real-world AI/ML use cases.
• Strong ability to work across GitHub and Azure DevOps workflows, including pull requests, version control, and delivery tracking.
• Demonstrated ability to communicate clearly with technical and non-technical stakeholders and to influence product decisions with evidence.
• Experience partnering cross-functionally with engineering, product, and business teams to move from problem framing to production decision-making.

Nice to have

• Familiarity with MLflow is strongly preferred.
• API design and integration.
• Model monitoring.
• Mentoring experience.
• Familiarity with cloud-native deployment patterns.

Details

• Location: Bangalore, Karnataka, India.