Strategic Data Science, Manager

Role Description: As a Strategic Data Scientist, you will own the end-to-end design, development, and production deployment of advanced AI and data-driven solutions. You’ll build scalable machine-learning models with large, heterogeneous datasets to solve complex business challenges and provide proactive, data-driven guidance to our Customer Success organization.

Key Responsibilities:

Collaborate with customer success, product, engineering, and sales teams to define KPIs and analytical approaches that answer key business questions

Design, build, and deploy machine learning and AI models (classification, regression, NLP, recommendation engines, etc.) to identify at-risk customers, predict attrition, and assess impact of product offerings

Develop customized recommendation engines that suggest next-best actions for customers (collaborative filtering, content-based, hybrid, graph-based techniques, etc.)

Drive the end-to-end machine learning lifecycle, from data preprocessing and feature engineering to model training, testing, and automated retraining workflows

Architect high-performance data pipeline for massive, multi-source datasets (streaming, batch, semi-structured), ensuring optimal storage, fast query performance, and high data integrity in hybrid cloud environments

Monitor production model performance by tracking key metrics like accuracy, drift, and latency. Leverage A/B testing and establish feedback loops to drive continuous improvement and rapid iteration

Support translation of strategic direction into analytical problems and actionable data science initiatives, ensuring data science alignment with organizational goals and long-term vision

Present clear, actionable insights and technical roadmaps to technical and non-technical stakeholders at all levels
 

Collaborative Partners:

Customer Success Leadership: define priority use cases and success metrics for AI-driven initiatives

Product & Engineering: embed data-science solutions into product features and roadmaps

Data Platform & MLOps: utilize internal infrastructure for data access, orchestration, and scalable deployments

Business Operations & Finance: validate model assumptions, quantify ROI, and support strategic planning
 

Role Requirements:

Education: Bachelor’s or Master’s in quantitative field such as Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related discipline

Experience: 2–5 years of hands-on experience building and deploying machine-learning solutions—especially recommender systems—in a SaaS or customer-facing environment

Technical Proficiency: Proficient in Python (or R) and ML frameworks (scikit-learn, TensorFlow, PyTorch); expertise with data tools (SQL, Spark, Airflow) and cloud platforms (AWS, GCP, Azure)

AI & Next-Gen Models: Demonstrated experience with embedding techniques, transformer-based models, and graph ML for large-scale recommendations

Business Acumen: Strong analytical mindset; able to translate model outputs into clear business recommendations and track impact through defined KPIs

Communication & Influence: Excellent at distilling complex technical concepts for non-technical audiences and driving alignment across teams

Self-Starter: Thrives in ambiguous environments; owns projects end-to-end and iterates based on feedback
 

Preferred Qualifications:

Enterprise-Scale Recommenders: Previous hands-on experience building and scaling recommender systems at major technology platforms

Top-Tier Consulting Background: Prior experience at a leading strategy firm with demonstrated ability to translate complex analysis into clear recommendations

LLM Proficiency: Hands-on experience leveraging large language models (e.g., GPT-4) for data augmentation, prompt engineering, or analytics automation

Advanced AI Use Cases: Proven track record of applying cutting-edge techniques—transformer fine-tuning, embedding retrieval, graph neural networks— to build production recommender or decision-support systems