Build, train, evaluate, and optimize machine‑learning models using Spark MLlib, Python, and cloud‑based toolchains
Perform exploratory data analysis (EDA), statistical profiling, and feature engineering on large-scale datasets hosted in Databricks
Implement and manage MLflow experiment tracking, model registry, versioning, and reproducibility workflows
Contribute to model monitoring, performance tuning, drift detection, and continuous improvement.
Develop notebooks, jobs, and workflows within Databricks for data preparation, model training, and batch/streaming inference
Build reusable, production‑grade feature pipelines in PySpark and SQL
Partner with Data Engineering, Analytics, Product, and SMEs to translate business problems into data‑driven solutions.

Requirements

Bachelor’s degree in Data Science, Computer Science, Analytics, Math, Statistics, Engineering, or related field, or related experience
Typically requires 2+ years of experience in applied ML, data science, or advanced analytics
Hands-on experience with Python, PySpark, SQL, and Git-based workflows
Practical exposure to cloud-based ML environments (preferably Databricks)
Understanding of ML techniques such as regression, classification, clustering, time-series forecasting, and embeddings
Ability to work with large, complex datasets.

Benefits

Competitive compensation package
Annual bonus opportunities
Flexible work arrangements
Professional development initiatives

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

machine learningSpark MLlibPythoncloud-based toolchainsexploratory data analysisfeature engineeringMLflowPySparkSQLdata preparation

Soft Skills

collaborationproblem-solvingcommunication

Certifications

Bachelor’s degree in Data ScienceBachelor’s degree in Computer ScienceBachelor’s degree in AnalyticsBachelor’s degree in MathBachelor’s degree in StatisticsBachelor’s degree in Engineering