Full Stack Data Science Engineer

Role Overview

• Design and operate sovereign data lake and warehouse architectures: schema design, data contracts, lineage tracking, freshness SLAs, governance frameworks, and multi-source ingestion pipelines.
• Build end-to-end data science systems: feature engineering, model training and evaluation, inference pipeline deployment, monitoring, and feedback loops.
• Develop novel statistical models, predictive algorithms, and optimization frameworks applied to operational data
• labor efficiency, asset performance, energy consumption, and SLA adherence.
• Identify patentable innovations in data processing architectures, model designs, and analytical methods; author invention disclosures and support patent prosecution alongside legal counsel.
• Create production-grade analytical products: executive KPI dashboards, real-time operational intelligence layers, forecasting tools, and anomaly detection systems.
• Translate raw operational data from physical infrastructure (sensors, CMMS, BMS, field logs) into structured, queryable, and model-ready data products.
• Establish data engineering best practices: dbt transformations, data quality tests, observability tooling, and documentation standards across the data platform.
• Collaborate with AI/ML engineers on feature stores, embeddings, and model-ready data products; partner with software engineers to integrate analytical outputs into user-facing applications.
• Drive data governance: access controls, PII handling, audit trails, and compliance with data sovereignty requirements.
• Architect and execute the migration from fragmented, siloed operational data systems to a decentralized federated data model
• implementing federated query engines (e.g., Trino, Spark, or equivalent) and data virtualization layers that enable cross-domain analytics without requiring full data centralization; design domain-oriented data products aligned with data mesh principles, preserving source-system ownership while enabling platform-wide discoverability and governed access.

Requirements

• 8+ years combining data science and data engineering in production environments, including at least 3 years at a senior IC level.
• Deep expertise in the Python data ecosystem: pandas, NumPy, scikit-learn, PyTorch or TensorFlow, and statistical modeling libraries.
• Proven experience designing and operating large-scale data warehouses or data lakes (Snowflake, BigQuery, Databricks, or equivalent).
• Strong SQL and transformation tooling (dbt, Spark, or similar); experience with streaming data pipelines (Kafka, Kinesis, or equivalent).
• Full-stack capability: able to take a data product from raw source through pipeline, model, API, and user-facing interface without hand-offs.
• Experience with intellectual property in the data or software domain: invention disclosures, prior art research, or patent application involvement.
• Strong technical writing skills
• able to articulate novel methodologies clearly for patent disclosures and analytical documentation.

Tech Stack

• BigQuery
• Kafka
• Numpy
• Pandas
• Python
• PyTorch
• Scikit-Learn
• Spark
• SQL
• Tensorflow

Benefits

• Health, dental, and vision insurance
• Health Savings Account (HSA)
• Gym discount
• Mental health benefits
• Discounted Group Life & AD&D
• Discounted Group Short & Long-term Disability
• 401(k) retirement matching
• PTO/paid holidays
• Parental leave