Data Science Engineer

About The Company Predictive Sales AI (PSAI) is a pioneering technology company dedicated to transforming digital marketing through advanced artificial intelligence solutions. Our innovative software leverages AI-powered analytics to enable home services businesses to make smarter, faster, and more informed decisions. By harnessing automation, predictive modeling, and data-driven insights, PSAI helps clients optimize their marketing strategies, improve operational efficiency, and drive sustainable growth. Our commitment to innovation and excellence positions us as a leader in the AI-driven marketing landscape, fostering a dynamic environment where cutting-edge technology meets real-world business needs.

About The Role The Data Science Engineer at PSAI will play a critical role in designing, developing, and maintaining the data and machine learning infrastructure that powers our predictive products. This position involves building scalable data pipelines, developing robust data models, and deploying machine learning solutions that support our business objectives. The ideal candidate will have a strong background in data engineering, proficiency in Python and SQL, and extensive experience working within Azure cloud environments. You will collaborate closely with cross-functional teams, including engineering, RevOps, and analytics, to ensure the reliability, accuracy, and efficiency of our data systems. Your work will directly impact the company’s ability to deliver innovative AI solutions that drive growth and customer success.

Qualifications

• Master’s degree in Data Science, Computer Science, Statistics, Engineering, or a related quantitative field (preferred)
• 4 years of experience in data engineering, ML engineering, or data platform development
• Minimum of 2 years experience deploying machine learning models into production workflows
• Expertise in SQL, including advanced analytics, recursive CTEs, and query optimization
• Proficiency in Python programming for data processing and modeling
• Hands-on experience with Azure Data Factory, Azure Synapse, Databricks, and related cloud tools
• Knowledge of distributed compute frameworks such as Spark, Dask, or Ray, with GPU acceleration experience a plus
• Experience building large-scale data pipelines (>100GB datasets) and warehouse systems
• Strong understanding of MLOps practices, including experiment tracking, model registry, and deployment
• Familiarity with object-oriented patterns and containerization using Docker
• Ability to communicate technical concepts effectively to non-technical stakeholders
• Demonstrated adaptability in fast-changing technical and business environments

Responsibilities

• Design and implement scalable batch and real-time data ingestion pipelines utilizing Azure Data Factory, APIs, event streams, and external connectors
• Develop ML-ready datasets from various sources including CRM, marketing automation platforms, product telemetry, and geospatial data
• Architect and optimize performant data warehouse and lakehouse systems in Azure Synapse or Databricks environments
• Train, validate, and deploy predictive models such as lead scoring, churn prediction, and forecasting through reproducible pipelines
• Build and maintain time-aware, leakage-resistant feature pipelines for production machine learning applications
• Support the full MLOps lifecycle, including experiment tracking, model registry, deployment, and monitoring using Azure Machine Learning
• Implement automated validation, anomaly detection, reconciliation, and monitoring to ensure data quality and pipeline integrity
• Establish and enforce data contracts to prevent upstream schema changes from disrupting downstream workflows
• Manage pipeline SLAs, set up alerting, incident response, and conduct postmortem analyses for continuous improvement
• Optimize data processing for large datasets through partitioning, incremental loads, distributed compute, and query tuning to improve performance and cost-efficiency
• Package models and pipelines using Docker containers to ensure consistent deployment across environments
• Collaborate with engineering teams on instrumentation and scalable data integration solutions
• Mentor junior engineers through code reviews, pairing, and knowledge sharing to foster team growth
• Communicate architectural decisions, technical trade-offs, and progress to stakeholders across business units

Benefits

• Competitive salary and performance-based bonuses
• Flexible work arrangements including remote and hybrid options
• Comprehensive health, dental, and vision insurance plans
• Paid time off and holidays to support work-life balance
• Opportunities for professional development and continuous learning
• Collaborative and innovative work environment that encourages creativity
• Access to cutting-edge tools and technologies in AI and data engineering

Equal Opportunity

Predictive Sales AI is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate based on race, ethnicity, gender, age, religion, sexual orientation, disability, or any other protected status. We encourage all qualified candidates to apply and join our innovative team dedicated to technological excellence and business impact.