Description

Key Responsibilities

· Analyze structured and unstructured datasets to identify trends, opportunities, and business insights. · Develop and deploy predictive, classification, clustering, and optimization models. · Apply machine learning, statistical analysis, and AI techniques to solve business problems. · Identify hidden patterns and relationships within complex datasets. · Support experimentation, hypothesis testing, and model validation activities. · Partner with business, operations, and technology teams to define analytical requirements and solutions. · Translate business challenges into data science use cases and analytical frameworks. · Generate insights and recommendations that improve operational performance and business outcomes. · Develop reusable analytical assets, models, and reporting frameworks. · Utilize AI/ML techniques including predictive analytics, anomaly detection, recommendation systems, NLP, and generative AI where appropriate. · Monitor and evaluate model performance and recommend enhancements. · Present analytical findings and business insights to stakeholders. · Contribute to data science best practices and continuous improvement initiatives. Required Qualifications · Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, or a related quantitative discipline. · 3+ years of experience in Data Science, Machine Learning, AI, or Advanced Analytics. · Strong foundation in statistics, predictive modeling, and machine learning. · Experience working with large-scale datasets. · Proficiency in Python, SQL, R, or similar analytical languages. · Experience with ML frameworks such as Scikit-learn, TensorFlow, PyTorch, XGBoost, or equivalent. · Knowledge of data visualization and analytical storytelling. · Strong problem-solving and communication skills. Preferred Qualifications · Experience with Generative AI, Large Language Models (LLMs), Agentic AI, or related technologies. · Experience building predictive or decision intelligence solutions. · Familiarity with cloud-based analytics platforms. · Experience with anomaly detection, recommendation engines, or intelligent automation solutions. · Experience working in large enterprise environments. Key Competencies Analytical Thinking Ability to analyze complex data and convert findings into meaningful business insights. Pattern Discovery Identify relationships, trends, and anomalies that drive business value. Business Problem Solving Translate business challenges into measurable analytical solutions. AI/ML Application Apply AI and machine learning techniques to improve decision making and operational efficiency. Data Storytelling Communicate complex findings clearly to technical and non-technical audiences. Collaborative Execution Work effectively across business, product, and technology teams. Success Profile The ideal candidate: · Uses data to uncover opportunities and risks. · Sees patterns beyond standard reporting. · Applies AI/ML techniques to solve real-world business problems. · Balances analytical rigor with business practicality. · Delivers actionable recommendations. · Contributes to a culture of data-driven decision making

Key Skills

Education

Bachelor's degree