gt-hq - Senior Data Scientist / ML Engineer (Forecasting) | NDA
Requirements
• Deployment through Asset Bundles • Proven experience building, deploying, and maintaining production ML solutions • Broad experience across multiple ML domains, including: • Forecasting / Time-Series Modelling • Classification • Gradient Boosting models (e.g. XGBoost, LightGBM) • Strong Python skills (Pandas, NumPy, scikit-learn, PyTorch) • Experience with model evaluation, performance monitoring, and accuracy metrics • Version control (Git) • Experience working with cloud environments (Azure preferred, AWS/GCP also considered) • Fluent English • Retail or similar consumer-facing industry experience • Azure DevOps: • Experience with Databricks model training and inference workflows • Databricks Apps and Lakebase • Experience with vector databases (Weaviate, Milvus) • Familiarity with LLM evaluation frameworks (e.g. DeepEval) • Strong sense of ownership and accountability • Proactive attitude and ability to work independently • Clear and confident communication with both tech and non-tech stakeholders • Comfortable working in ambiguity and helping define requirements • Strategic thinking and focus on business impact • INTERVIEW STEPS • 1. GT interview with Recruiter • 2. Technical interview • 3. Cultural fit interview • 4. Final interview • 5. Reference check • 6. Security check
Responsibilities
• Design, train, and deploy ML models for time-series forecasting and related data tasks • Build and maintain data pipelines using cloud-native tools (AWS, GCP, or Azure) • Develop and optimize forecasting models (Prophet, ARIMA, LSTM, TimeGPT) • Collaborate with data, product, and cloud engineers to deliver reliable, scalable solutions • Participate in different stages of the project lifecycle - from discovery and PoC to production deployment, presenting your work to stakeholders • Work closely with business stakeholders and SMEs to gather requirements, shape solutions, and drive project discovery • Communicate modelling approaches, assumptions, and results to both technical and non-technical audiences • ESSENTIAL KNOWLEDGE, SKILLS & EXPERIENCE (MUST-HAVE): • 4+ years of commercial experience in Data Science / Machine Learning
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