We are seeking a highly skilled Data Scientist with strong expertise in Machine Learning, Generative AI (GenAI), and predictive analytics to design and deploy enterprise-grade AI solutions. The role involves building end-to-end ML and GenAI systems for forecasting, optimization, explainability, and intelligent decision support, while translating complex data insights into measurable business impact.
Key Responsibilities:
Machine Learning & Advanced Analytics ->Design, develop, and deploy supervised and unsupervised ML models including Logistic Regression, Decision Trees, Random Forests, SVM, K-Means, and time-series models. ->Build predictive models for KPI degradation, demand/price forecasting, classification, clustering, and anomaly detection. ->Perform feature engineering, EDA, statistical analysis, hypothesis testing, and ANOVA to drive actionable insights. ->Optimize model performance through class balancing, threshold tuning, and evaluation metrics to reduce business risk and cost.
Generative AI & Intelligent Systems ->Develop GenAI-driven forecasting and decision-support frameworks using LLMs (GPT-4 / GPT-4o-mini). ->Implement Retrieval-Augmented Generation (RAG) pipelines using Vector Databases and embeddings. ->Build agentic AI workflows for automated planning, scheduling, and inference. ->Deliver GenAI-powered explainability using SHAP + LLM-based narrative insights.
Data Engineering & Platforms ->Work with large-scale data using Python, SQL, PySpark, and Azure Databricks. ->Build scalable pipelines for data ingestion, transformation, and model deployment. ->Build agentic AI workflows for automated planning, scheduling, and inference.
Visualization & Business Enablement ->Develop dashboards and reports using Power BI and visualization libraries. ->Communicate model outputs, risks, and recommendations clearly to stakeholders. ->Partner with product, engineering, and business teams to align AI solutions with real-world requirements.
Key Achievements (Representative Impact) ->Improved RCA efficiency by 35% using ML-based KPI degradation prediction. ->Reduced CAPEX by 12% through optimized classification thresholds. ->Achieved >95% directional accuracy in commodity price forecasting. ->Improved forecast accuracy by 15% using GenAI + news impact scoring. ->Delivered AI-driven scheduling automation, generating L1–L3 project plans compatible with Primavera P6. ->Enabled 28% operational cost savings via statistical analysis and optimization.
Soft Skills ->Strong analytical and problem-solving mindset ->Ability to translate complex models into business insights ->Excellent collaboration with cross-functional teams ->Continuous learner with interest in emerging AI technologies
What We Offer:
Opportunity to work on cutting-edge initiatives with industry-leading teams.
Competitive compensation and benefits package.
Flexible work arrangements and professional development opportunities.
Collaborative, innovative-driven work environment.
