Develop and deploy advanced statistical models and machine learning algorithms
Perform exploratory data analysis to uncover trends, patterns, and anomalies in large datasets
Build predictive models to forecast key metrics and guide business strategy
Design and maintain scalable data and model pipelines
Collaborate with data science team and business analysts to understand data needs

Requirements

Master’s or Ph.D. in Data Science, Statistics, Computer Science, or a related field
5+ years in data science, with a proven track record of delivering impactful insights and models
Experience with large-scale datasets and distributed computing tools e.g. Snowflake, Azure Data Warehouse, Databricks, and Spark
Strong knowledge of SQL and relational databases
Proficient in programming languages such as Python, including use of notebooks, tabular data (Pandas, Spark) and integration with LLMs (Langchain, MCPs)
Expertise in machine learning frameworks (e.g., TensorFlow, PyTorch) and libraries (e.g., scikit-learn, PyMC, Stan)
Model development and maintenance (MLFlow), and data lifecycle management
Proven ability to optimize model performance for business outcomes
Proficiency in LLM prompt engineering

Benefits

Applicant Tracking System Keywords

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

Hard Skills & Tools

statistical modelsmachine learning algorithmsexploratory data analysispredictive modelsSQLPythonPandasTensorFlowPyTorchLLM prompt engineering

Soft Skills

collaborationcommunicationproblem-solvinganalytical thinking

Certifications

Master’s in Data SciencePh.D. in Data ScienceMaster’s in StatisticsMaster’s in Computer Science