Associate Data Scientist

Associate Data Scientist (Machine Learning \& Statistics)

**Department: Information Technology

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

Role Overview

**We are looking for an Associate Data Scientist with strong core machine learning fundamentals and hands-on experience, particularly in time series data, to work end-to-end on real-world ML problems.

The role involves data exploration, model development, experimentation, deployment support, and performance tracking, with direct business impact.**

Exposure to LLMs / RAG systems and basic water or process-domain knowledge is a plus, but the primary focus remains classical ML done well.

Key Responsibilities

• Work hands-on with raw datasets to explore data, frame problem statements, and build strong baseline models
• Design, train, validate, and evaluate ML models, with rigorous metric tracking
• Extensively work with time series data (trend, seasonality, lag features, forecasting, anomaly detection)
• Run structured experiments and ablation studies to improve model performance
• Collaborate with engineering, product, and operations teams to integrate models into applications
• Support model deployment, monitoring, and performance tracking in production environments
• Clearly communicate insights, results, and limitations to both technical and non-technical stakeholders

Must-Have Skills \& Experience

• 1+ year of hands-on experience in Data Science / Machine Learning
• Strong Python proficiency (pandas, numpy, scikit-learn)
• Strong core machine learning fundamentals, including hands-on experience with regression and classification techniques, a solid understanding of the bias-variance tradeoff, feature engineering, and model evaluation using appropriate performance metrics.
• Extensive experience working with time-series data, encompassing forecasting, trend and seasonality analysis, creation of lag and rolling features, and the application of time-aware validation strategies.
• Strong understanding of statistics and quantitative reasoning
• Good grasp of model interpretability, bias, and fairness concepts
• Strong problem-solving ability and ownership mindset
• 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, such as 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