Full Stack Data Science Engineer

Salute Inc.•United States, US,

8h•$120,000 - $150,000

About The Position

Salute is a leading provider of cutting-edge Data Center Infrastructure Services, dedicated to serving data center clients worldwide. We pride ourselves on delivering sustainable solutions, unparalleled reliability, and outstanding customer service. As we continue to grow, we are seeking a dynamic and experienced Full Stack Data Science Engineer to join our team and drive our relationships with hyperscale clients to new heights. We are seeking a Full Stack Data Science Engineer who builds end-to-end data systems - from sovereign data lake architecture and pipeline engineering through predictive model development, deployment, and client-facing analytical products - and who actively converts novel methodologies into protected intellectual property. You will define the data foundation that powers operational intelligence across thousands of mission-critical infrastructure sites, develop patentable analytical approaches, and work with patent counsel to protect them. This role requires both rigorous data science depth and the full-stack engineering capability to ship production-grade data products independently.

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

Nice To Haves

Named inventor on data science, ML, or software patents.
Domain experience in operations-heavy industries: data center management, facilities, manufacturing, energy, logistics, or industrial IoT.
Experience building client-facing BI or analytics products; familiarity with tools such as Power BI, Tableau, or custom React-based dashboard frameworks.
Knowledge of CMMS systems, BMS data formats, SCADA historian data, or similar operational technology data sources. Familiarity with xAPI / LRS standards for operational event logging and workforce analytics.
Experience with MLOps platforms (MLflow, Weights & Biases, SageMaker) and model governance practices.
Hands-on experience with federated query engines (Trino, Presto, DuckDB), data virtualization platforms, or data mesh implementations that unify access across siloed operational systems without full data movement.
Familiarity with open table formats (Apache Iceberg, Delta Lake, Apache Hudi) as the foundation for interoperable, decentralized data lakes.
Graduate degree in Data Science, Statistics, Computer Science, Applied Mathematics, or related field.

Responsibilities

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.