Associate Data Scientist

Overview

• 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
  
  

• 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
  
  

• 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
  
  

• 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