Associate Data Scientist (Machine Learning & Statistics)

Department: Information Technology

Work Schedule: 9:30 AM - 6:30 PM (Monday-Friday)

Role Overview

Whitefield Careers is seeking an Associate Data Scientist with robust machine learning fundamentals and practical experience, particularly in time series data. This role offers the opportunity to work end-to-end on real-world ML problems, contributing directly to business impact. Responsibilities include data exploration, model development, experimentation, deployment support, and performance tracking.

While exposure to LLMs / RAG systems and basic water or process-domain knowledge is advantageous, the primary focus remains on excelling in classical machine learning.

Key Responsibilities

• Engage hands-on with raw datasets for exploration, problem framing, and building strong baseline models.
• Design, train, validate, and evaluate ML models, employing rigorous metric tracking.
• Conduct extensive work with time series data, covering trend, seasonality, lag features, forecasting, and anomaly detection.
• Execute structured experiments and ablation studies to enhance model performance.
• Collaborate with engineering, product, and operations teams to integrate models into applications.
• Provide support for model deployment, monitoring, and performance tracking in production environments.
• Clearly articulate insights, results, and limitations to both technical and non-technical stakeholders.

Must-Have Skills & Experience

• Minimum 1 year of hands-on experience in Data Science / Machine Learning.
• Strong Python proficiency, including libraries such as pandas, numpy, and scikit-learn.
• Solid foundation in core machine learning principles, with practical experience in regression and classification techniques, a deep understanding of the bias-variance tradeoff, feature engineering, and model evaluation using appropriate performance metrics.
• Extensive experience with time-series data, including forecasting, trend and seasonality analysis, creation of lag and rolling features, and application of time-aware validation strategies.
• Strong understanding of statistics and quantitative reasoning.
• Good grasp of model interpretability, bias, and fairness concepts.
• Excellent problem-solving abilities and a strong sense of ownership.
• Clear written and verbal communication skills.

Good to Have (Nice-to-Have, Not Mandatory)

• Basic understanding of water/wastewater domain concepts, KPIs, or industrial processes.
• Foundational exposure to MLOps practices, including model packaging, API-based model serving, experiment tracking, and basic model monitoring.
• Experience with chemical or process analytics.

Qualifications

• Degree in Computer Science, Statistics, Mathematics, Chemical Engineering, Applied Chemistry, or a related field, or equivalent practical experience demonstrating similar technical expertise.
• 1+ years of relevant professional experience in applied data science or machine learning.