Principal Data Scientist CX360(Retail / E-commerce)

About the project

We're searching for a dynamic Lead Data Scientist focusing on rapid feature and algorithm implementation to spearhead Retail/E-commerce Solution Accelerator for customer lifetime value (CLV) maximization. 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

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.

• 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 Practical Experience in Retail/E-commerce Data Science Projects/Products: A track record of implementing models in a retail/e-commerce setting can provide insights that are not easily learned from books or courses.

• Experience in leading data science and AI engineering teams 3+ years

• Experience in a fast-moving startup (B2B, A/B/C/D - rounds) or e-fast e-commerce companies environment is a must

• Nice to have

  • Publications: Having authored or co-authored papers in the area of recommenders or related AI fields.

  • AI/ML Competitions & Conferences: Participation in notable competitions like Kaggle, RecSys, which showcases hands-on expertise.

Terms & conditions

Allocation: 0.5+ FTE

Time zone: preferably Europe

Candidate’s location: preferably Europe

Start date: October 2023