Shivangi Dhakad

Leading Data, ML & GenAI product and engineering and owning the enterprise AI strategy for Eneco - bridging the gap between tech and the business to turn Data & AI capability into measurable commercial and customer impact. Owning roadmaps, business integrations and delivery for the full platform stack that powers forecasting, customer intelligence, experimentation and enterprise Gen-AI use cases. Areas and teams: Gen-AI (Product & Adoption units), ML platform, Analytics & Experimentation, Data Infrastructure(Snowflake, Databricks, Collibra, Airflow, DBT), Data Governance and Meter Data as a central data product.

I advise startups and scale-ups on: - Data & ML engineering strategy - GenAI adoption and product integration - Building high-performing data and ML teams - Designing scalable data architectures - Prioritization, roadmap planning, and org design for data and AI initiatives. I also mentor professionals and aspiring leaders who are: - Making a career transition into Data, ML, or GenAI roles - Navigating challenges in engineering leadership or team growth - Looking for strategy-oriented coaching to expand beyond hands-on coding - Preparing for interviews or designing career roadmaps

Led teams of Engineers to solve cross-track Data, Analytics & ML Engineering challenges for Marketplace Business Unit, building modernized customer data products on the cloud to enable holistic cross-product customer journeys. Functioned as an interim SEM in 2023 and 2024 partly, working with Data at the petabyte scale, owned roadmaps for order and customer data products & delivery, collaborating closely, Data Analytics and Data Science functions to deliver business value.

- Developed and scaled the recommendation engine for Microsoft Teams Feeds, enabling personalized enterprise content discovery in a workplace application using Data Science & Machine Learning modeling (GBM), to support daily active users from 1M to 50M in 2 years. Contributed in 2 patents. - Designed and implemented big data pipelines in Scope(similar to dbt) & Cosmos(Azure) for feature engineering, model training, and KPI computation, optimizing data processing for over 1 billion actions daily. - Automated and scaled pipelines, reducing average run time by 10x, improved model recall and precision. Contributed to the high and low-level design of backend distributed systems for intelligent feeds.