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Data Science Engineer Predictive Sales

AI - 3.1

Boca Raton, FL Job Details Full-time 13 hours ago Benefits Opportunities for advancement Qualifications AI models GPU programming Commercial use (data warehousing systems) Performance tuning Software engineering Databricks Data Integration (Data management) Cloud data warehouses PyTorch Spark Application deployment AI platforms (beyond public GPTs) Scalable systems SQL Pandas Azure Synapse Analytics Production systems Machine learning cloud services Machine intelligence Docker Azure Key Vault Model deployment Azure DevOps proficiency Azure Data Factory Version control systems Query execution time improvement Azure Machine Learning Marketing automation Scalability Distributed computing Full Job Description Full Time position in 120 E Palmetto Park Road Boca Raton, FL, 33432

Job Title:

Data Science Engineer Location:

Boca Raton, FL-Remote/Hybrid About Us:

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. We are seeking a Data Science Engineer with strong data engineering and MLOps expertise to build scalable, production-grade ML and data platforms that directly impact customer growth and retention.

Job Overview:

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.

This role requires:

Key Responsibilities:

4+ years in data-centric engineering Proven experience deploying ML models via pipelines 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. Maintain clean, testable, production-ready Python codebases using: Object-oriented patterns Type hinting CI/CD workflows via Azure DevOps 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, RevOps, and leadership. Partner with Engineering on instrumentation and scalable data integration. Mentor junior engineers through pairing, code reviews, and documentation best practices.

Desired Traits:

We are looking for an individual who is organized, proactive, and detail-oriented. In this role, you will work closely with teams across the company. Here's what we're looking for: 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, RevOps, and analytics Documents well and supports others through reviews/mentorship

Required Skills and Experience:

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. Python (Expert): pandas, polars, scikit-learn; PyTorch, transformers; production engineering (OOP, testing, typing) SQL (Expert): advanced analytics, recursive CTEs, query tuning, Azure Synapse optimization

Azure Data & ML Stack:

Data Factory (ETL/ELT), Azure ML (MLOps), Key Vault, Databricks/Spark, Docker deployment

Distributed & Large-Scale Compute:

Spark, Ray, Dask; GPU acceleration with RAPIDS (plus)

Geospatial & Specialized Data:

GeoPandas, Shapely, rasterio

AI Automation & LLMs:

LangChain/Semantic Kernel, agentic workflows

DevOps & CI/CD:

Azure DevOps pipelines, Gitflow, rebasing, clean version control Why Join Us?

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! Apply for Data Science Engineer Complete Online Application Please complete the application to apply for Data Science Engineer at Predictive Sales AI, LLC.