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 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
