Data Science Specialist

Data Science Specialist — Job Description

Summary

Uses statistical modeling, machine learning, and data analysis to generate insights, build predictive models, and support data‑driven decision‑making across the organization.

Key Responsibilities

• Design, develop, and validate predictive and prescriptive models (classification, regression, time series, recommendation systems).
• Perform exploratory data analysis, feature engineering, and statistical testing to translate business questions into analytical solutions.
• Develop end‑to‑end ML pipelines: data ingestion, preprocessing, model training, evaluation, and deployment.
• Collaborate with product, engineering, and analytics teams to define metrics, success criteria, and production requirements.
• Implement model monitoring, performance tracking, drift detection, and periodic retraining strategies.
• Communicate results and insights through visualizations, reports, and presentations to technical and non‑technical stakeholders.
• Maintain reproducible experiments and model documentation (notebooks, model cards, datasheets).
• Ensure data quality, privacy, and compliance when working with sensitive datasets.
• Contribute to data collection strategy and instrumentation to improve model inputs and observability.
• Mentor junior data scientists/analysts and participate in code/research reviews.

Required Qualifications

• Bachelor’s degree in Data Science, Statistics, Computer Science, Mathematics, or related field (MS preferred for senior roles).
• 2+ years experience applying statistical and machine learning methods to real‑world problems (adjust by seniority).
• Proficiency in Python (pandas, scikit‑learn, PyTorch/TensorFlow) or R, and strong SQL skills.
• Solid understanding of statistics, probability, experiment design/A‑B testing, and model evaluation metrics.
• Experience with data engineering tools and workflows (ETL, batching/streaming, Airflow/Prefect).
• Practical experience deploying models to production (APIs, model serving frameworks, MLOps practices).
• Strong data visualization skills (Matplotlib/Seaborn, Plotly, or BI tools like Looker/Tableau).
• Clear communication and stakeholder‑facing presentation skills.

Preferred Qualifications

• Experience with advanced ML areas: deep learning, NLP, computer vision, or recommender systems as relevant to the role.
• Familiarity with cloud ML platforms and tools (SageMaker, Vertex AI, Azure ML) and feature stores.
• Knowledge of model governance, fairness, explainability tools (SHAP, LIME, ELI5), and privacy‑preserving techniques.
• Experience with containerization and orchestration (Docker, Kubernetes) and Infrastructure as Code.
• Publications, competition placements (Kaggle), or open‑source contributions are a plus.

Pay: $64.32 – $95.23 per hour

Benefits:

• Dental insurance
• Health insurance
• Salary packaging

Work Location: In person