CX360 Principal Data Scientist

About the project

We're searching for a dynamic Lead Data Scientist focusing on rapid feature and algorithm implementation to spearhead Projects and Solution Accelerators for customer lifetime value (CLV) maximization in Retail/E-commerce. This role demands a deep understanding of retail/e-commerce dynamics and an agile approach to developing and deploying critical data models.

Today’s AI solutions targeting customer engagement are focused on click-through-rate optimization - raking systems, recommender systems, etc. This helps to optimize engagement at the moment, but this myopic view doesn’t consider long-term goals and customer retention. We are building a new layer for CLV optimization with reinforcement learning on top of existing solutions.

Duration: 6+ months
Stage: solution development from scratch

Objective + KPIs

• The solution is validated with 3+ new customers with CSI 100%

• 80% of AI engineering best practices are applied to the solution

Areas of Responsibility

• AI solution architecture design and roadmap planning

• Engineering team leadership and performance management

• Communication with the customer on the development progress

• AI solution technical quality and performance management

Skills

• Proficiency in Python for quickly implementing machine learning algorithms

• Expertise with experimentation frameworks (SciPy, PyMC3, Spark MLlib) and analytics tools to quickly run robust tests and analyze results. The ability to design and analyze multivariate, non-standard experiments.

  • Example:

    • Leveraged Bayesian hierarchical models in PyMC3 to implement multivariate uplift modeling in Spark to test effectiveness of personalized email campaigns. Quantified incremental response rates.

    • Built out an experimentation framework on SciPy and statsmodels to test the effects of recommendation algorithms, email timing, and coupon targeting on customer conversion rates. Used MANOVA to model the interactions.

    • Developed an revenue optimization engine using contextual bandits algorithms with Spark MLlib. Evaluated performance through A/B testing framework on user randomization.

• Strong optimization algorithms abilities, including reinforcement learning, bandits, and uplift modeling.

  • Reinforcement Learning - Ability to implement temporal difference learning, deep Q-learning, policy gradient methods to optimize long-term rewards. Critical for modeling customer interactions over time.

  • Contextual Bandits - Expertise with bandit algorithms like upper confidence bound, LINUCB, and Thompson sampling to optimize actions based on user context. Key for personalization.

  • Uplift Modeling - Proficiency in techniques like two-model, meta-learner uplift to identify causal impacts of interventions. Crucial for targeting high incremental value customers.

• Data Preprocessing and Engineering: Mastery in transforming complex retail/e-commerce datasets into model-ready formats. Example: Engineered features from raw transaction logs that improved a churn prediction model's accuracy by 20%.

• Interdisciplinary Collaboration: Working efficiently with software engineers, data scientists, stakeholders, etc.

• Communication: Clear and concise communication, especially of complex technical concepts to non-technical stakeholders.

Knowledge

• Deep Understanding of Retail/E-commerce Metrics and Dynamics: Knowing the specifics of retail such as seasonality, purchasing behavior, customer segments, etc.

• Propensity, Uplift, and CLTV Prediction Methodologies: Comprehensive knowledge of these models' theoretical underpinnings and latest trends.

• State-of-the-Art RL Techniques for Optimization: Awareness of current advancements like Deep Reinforcement Learning, Proximal Policy Optimization, etc.

• Advanced Bandit Strategies: Understanding of variations like epsilon-greedy, UCB (Upper Confidence Bound), Thompson sampling, etc.

Experience

• 5+ Years in Retail/E-commerce Data Science: With a focus on personalization projects/products. A track record of implementing Solutions can provide insights that are not easily learned from books or courses.

• Uplift Modeling and Causal Inference: Proven experience in uplift modeling, essential for