Lead Data Scientist, AI/ML_Remote_WFH

Key Responsibilities

Advanced Statistical Analysis & Modeling

• Conduct sophisticated statistical analyses, including multivariate analysis, time series modeling, survival analysis, and causal inference studies.
• Design and implement A/B testing frameworks with proper statistical rigor, power analysis, and multiple testing corrections.
• Build advanced machine learning models for customer segmentation, personalization, recommendation systems, and operational optimization.
• Perform causal inference analysis using techniques like difference-in-differences, instrumental variables, and propensity score matching to measure true business impact.

Business Intelligence & Insights Generation

• Partner with executive leadership to identify strategic questions and translate business challenges into analytical frameworks.
• Develop comprehensive dashboards and reporting systems that provide actionable insights for various stakeholders.
• Conduct deep-dive analyses on customer behavior, market trends, competitive positioning, and operational performance.
• Generate data-driven recommendations that directly influence product development, marketing strategy, and operational decisions.

Customer Analytics & Behavioral Insights

• Design and implement customer segmentation strategies using advanced clustering techniques, behavioral analysis, and predictive modeling.
• Develop customer lifetime value models and retention strategies based on a comprehensive analysis of customer interactions and transactional data.
• Analyze customer journey optimization across all touchpoints to identify friction points, improvement opportunities, and personalization strategies.
• Build predictive models for customer behavior, including churn prediction, upsell/cross-sell opportunities, and satisfaction forecasting.

Experimentation & Causal Analysis

• Design and analyze controlled experiments to measure the impact of product changes, marketing campaigns, and operational improvements.
• Implement advanced experimental designs, including factorial experiments, multi-armed bandits, and switchback tests.
• Conduct causal inference studies to understand the true impact of business initiatives and separate correlation from causation.
• Develop measurement frameworks for attribution modeling, incrementality testing, and marketing mix optimization.

Required Skills & Experience

Machine Learning & Predictive Modeling

• Classical ML Algorithms:

  Advanced understanding of linear/logistic regression, decision trees, ensemble methods, SVM, and clustering.

• Deep Learning:

  Experience with neural networks, CNNs, RNNs, and transformers for both structured and unstructured data.

• Advanced Ensemble Methods:

  Knowledge of stacking, blending, Bayesian model averaging, and custom ensemble architectures.

• Feature Engineering:

  Proficiency in automated feature generation, selection techniques, and dimensionality reduction.

Programming & Data Manipulation

• Python Expertise:

  Advanced proficiency with the scientific computing stack, including NumPy, Pandas, SciPy, and Scikit-learn.

• Statistical Software:

  Expert-level proficiency in R for statistical analysis and visualization.

• SQL Mastery:

  Experience with complex query optimization, window functions, and database design principles.

• Big Data Technologies:

  Familiarity with Spark (PySpark/SparkR), the Hadoop ecosystem, and other distributed computing frameworks.

• Cloud Platforms:

  Experience with AWS (SageMaker, Redshift, S3), GCP (BigQuery, Vertex AI), or Azure (Synapse, ML Studio).

Modern AI & Generative AI Capabilities

• Large Language Models:

  Practical experience with GPT-4, Claude, and Gemini for data analysis augmentation and insight generation.

• Prompt Engineering:

  Advanced prompting techniques for analytical tasks, report generation, and hypothesis formulation.

• AI-Assisted Analytics:

  Using LLMs for data exploration, code generation, and analytical workflow automation.

Education & Experience

• Experience:

  5-8 years of progressive experience in data science, analytics, or quantitative research roles.

• Education:

  A master's degree in Statistics, Mathematics, Economics, Computer Science, Physics, or a related quantitative field is required.

• Proven Track Record:

  A proven history of delivering high-impact analytical projects that directly influenced business decisions and outcomes is essential.

• Industry Experience:

  Experience in customer service, telecommunications, SaaS, or related customer-centric industries is strongly preferred