Data Science Engineer

Job Title:
Data Science Engineer

Location:

Boca Raton, FL-Remote/Hybrid

At Predictive Sales AI (PSAI), we’re redefining how technology and intelligence transform digital marketing. Our AI-powered software enables home services businesses to make smarter, faster decisions—fueling growth through automation, prediction, and precision.

As a Data Science Engineer
, you will design and operate the data + machine learning foundations behind PSAI’s predictive products. You will build scalable pipelines and robust warehouse/lakehouse models across CRM, marketing, product events, and external datasets — ensuring reliability, accuracy, and business continuity at scale.

• 4+ years in data-centric engineering
• Deep expertise in Python, SQL, and Azure infrastructure
• Architectural ownership through data contracts and resilient modeling

• Build scalable batch and near-real‑time ingestion pipelines using Azure Data Factory , APIs, event streams, and external connectors.
• Develop ML-ready datasets across CRM, marketing automation platforms, product telemetry, and geospatial data sources.
• Design performant, well‑modeled warehouse/lakehouse systems in Azure Synapse or Databricks.
• Train and deploy predictive models (lead scoring, churn prediction, forecasting) through reproducible pipelines.
• Build time-aware, leakage-resistant feature pipelines for production ML use cases.
• Support full MLOps lifecycle using Azure Machine Learning , including experiment tracking, model registry, and deployment.
• Implement automated validation, anomaly detection, reconciliation, and monitoring for pipelines and warehouse models.
• Design and enforce data contracts to prevent upstream schema changes from breaking downstream ML workflows.
• Own pipeline SLAs, alerting, incident response, and durable improvements through postmortems.
• Optimize processing for very large datasets (>100GB) through partitioning, incremental loads, distributed compute, and query tuning.
• Improve cost efficiency across compute/storage in Azure environments.
• Object-oriented patterns
• Package models and pipelines using Docker for consistent deployment across dev/staging/prod.
• Communicate architectural trade-offs and technical debt in business terms to Product, Rev Ops, and leadership.
• Partner with Engineering on instrumentation and scalable data integration.
• Mentor junior engineers through pairing, code reviews, and documentation best practices.

• Ownership mindset with a reliability-first approach
• Strong SQL/Python and a high attention to data quality
• Scales systems thoughtfully (performance/cost aware, maintainable designs)
• Collaborative communicator across engineering, Rev Ops, and analytics
• Documents well and supports others through reviews/mentorship

• Preferred Master’s degree in Data Science, Computer Science, Statistics, Engineering, or a closely related quantitative field.
• 4+ years in data engineering, ML engineering, or data platform development.
• Minimum 2 years deploying ML models into production workflows.
• Experience building pipelines and warehouse systems at scale (>100GB datasets).
• Demonstrated adaptability in fast-changing technical and business environments.
• SQL (Expert): advanced analytics, recursive CTEs, query tuning, Azure Synapse optimization
• Distributed & Large-Scale Compute:
  Spark, Ray, Dask; GPU acceleration with RAPIDS (plus)
• AI Automation & LLMs:
  Lang Chain/Semantic Kernel, agentic workflows

• Innovative Environment:Be part of a forward-thinking company that values creativity and encourages the exploration of new ideas.
• Professional Growth:Access opportunities for continuous learning and career advancement within a supportive and dynamic team.
• Comprehensive Benefits:Enjoy a competitive salary, performance-based bonuses, flexible work arrangements, and a robust benefits package.
• Collaborative Culture:Work in a team-oriented environment where collaboration and mutual respect drive our success.

If you're ready to be part of an innovative, growth-oriented team, apply today!

Hours 40

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