Responsibilities:

·        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

Skills: 

·        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; familiarity with MLflow is strongly preferred

·        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

Preferred Skills:

API design and integration, model monitoring, mentoring experience, familiarity with cloud-native deployment patterns