Job Description

Senior Hands-on Engineering Leader – Predictive AI & Forecaster

Position Summary

We are seeking a highly skilled Senior Hands-on Engineering Leader – Predictive AI & Forecasting to design, develop, and implement advanced predictive models and forecasting solutions for complex business challenges.

The ideal candidate will possess strong expertise in statistical modeling, machine learning, predictive analytics, and time-series forecasting, with a proven track record of delivering solutions from initial problem formulation to production deployment and measurable business impact.

This role demands a blend of advanced analytical skills, software engineering discipline, business acumen, and scientific rigor. The individual will independently address complex data science problems, evaluate methodologies, and create scalable, reliable, and interpretable predictive solutions.

Key Responsibilities

Predictive AI & Advanced Analytics

• Design, develop, validate, and deploy predictive models for complex business challenges.
• Utilize advanced statistical and machine learning techniques to forecast outcomes, behaviors, risks, and business events.
• Create solutions for regression, classification, risk prediction, anomaly detection, and behavioral prediction.
• Identify significant predictive signals from large-scale datasets.
• Develop models that enhance data-driven decision-making and yield measurable outcomes.
• Assess model performance using statistical measures and relevant business KPIs.
• Implement model explainability, interpretability, and uncertainty estimation techniques.

Time Series Forecasting

• Lead the development of advanced forecasting solutions for various time-series data.
• Generate forecasts based on business needs.
• Analyze trends, seasonality, cyclicality, and temporal dependencies.
• Address forecasting challenges such as missing observations and changing patterns.
• Develop and evaluate statistical, machine learning, and hybrid forecasting approaches.
• Incorporate relevant exogenous variables into forecasts.

Statistical & Machine Learning Modeling

• Select and apply suitable modeling techniques based on data characteristics and business needs.
• Utilize methods including ARIMA, Exponential Smoothing, Regression, Random Forest, and LSTM.
• Establish baseline models and demonstrate improvements through experimentation.

Data Preparation & Feature Engineering

• Conduct exploratory and statistical analysis of datasets.
• Create robust temporal and predictive features.
• Identify and resolve data quality issues.
• Integrate internal and external data sources to enhance performance.
• Develop scalable methods for feature generation.

Model Validation & Evaluation

• Establish rigorous model development and validation methodologies.
• Implement time-aware cross-validation and backtesting as necessary.
• Evaluate models using metrics like MAE, RMSE, and Precision.
• Conduct residual and sensitivity analysis.
• Ensure controls are in place to prevent data leakage.

Productionization & MLOps

• Translate data science solutions into reliable production systems.
• Collaborate with cross-functional teams to operationalize models.
• Develop production-ready workflows for training and inference.
• Monitor model accuracy and support automated retraining.
• Ensure solutions are maintainable and scalable.

Technical Leadership & Collaboration

• Lead data science initiatives from problem definition to implementation.
• Provide technical guidance on predictive modeling and analytical approaches.
• Translate business needs into data science solutions.
• Communicate findings to technical and non-technical audiences.
• Mentor Data Scientists and contribute to best practices.
• Evaluate emerging technologies in Predictive AI and machine learning.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, Operations Research, or Economics.
• More than 15 years professional experience and 10+ years of experience in Data Science, Machine Learning, or a related area.
• Hands-on experience with predictive modeling and time-series forecasting.
• Strong foundation in statistics and machine learning.
• Advanced proficiency in Python and SQL.
• Experience with Python data science and machine learning libraries.
• Proven experience in production deployment of models.
• Excellent analytical and communication skills.

Preferred Qualifications

• Master's or PhD in a quantitative field.
• Experience with large-scale forecasting systems.
• Familiarity with hierarchical or probabilistic forecasting.
• Experience with deep learning for sequential data.
• Knowledge of Transformer-based approaches for time series.
• Experience in causal inference or causal machine learning.
• Familiarity with PyTorch or TensorFlow.
• Experience with Spark / PySpark.
• Familiarity with cloud platforms like AWS or Azure.
• Experience with MLOps and automated model lifecycle management.
• Research publications or applied research experience are a plus.

Technical Skills

Programming & Data: Python, SQL, Pandas, NumPy

Machine Learning: Scikit-learn, XGBoost, LightGBM, CatBoost

Statistical Modeling: Statsmodels, ARIMA, SARIMA, SARIMAX, ETS

Deep Learning: PyTorch, TensorFlow, LSTM, GRU, Transformers

Forecasting: Time Series Analysis, Demand Forecasting, Probabilistic Forecasting, Backtesting

MLOps & Engineering: MLflow, Model Monitoring, CI/CD, Cloud Platforms