Stensul is looking to leverage data assets to drive strategic business outcomes, focusing on critical areas like customer behavior, pricing optimization, and business forecasting.
Requirements
- Advanced Modeling Expertise: Deep experience designing, implementing, and validating predictive models, including specific expertise in churn modeling, pricing optimization, and cluster/segmentation analysis (e.g., K-Means, DBSCAN, hierarchical clustering).
- Programming & Statistics: Expert proficiency in Python (including packages like Scikit-learn, Pandas, Statsmodels) and SQL.
- Data Application Development: Proven experience building and deploying interactive data applications or internal tools using frameworks like Streamlit, Dash, or Gradio.
- NLP/LLMs (Desired): Practical experience with Natural Language Processing techniques (e.g., sentiment analysis, topic modeling, named entity recognition) and familiarity with architectures and use cases of Large Language Models (LLMs).
- MLOps and Deployment: Familiarity with model versioning, testing, and deployment processes within an MLOps framework (e.g., using tools like MLflow, Airflow, or Kubeflow).
- Statistical Rigor & Business Acumen: Strong ability to translate ambiguous business problems into well-defined analytical projects with measurable impact and communicate complex statistical concepts clearly.
- Autonomous & Collaborative: Comfortable leading complex projects independently while effectively collaborating with Data Engineering, Product, and GTM teams.
Responsibilities
- Designing, developing, and validating advanced statistical and machine learning models to solve high-impact business problems, such as churn prediction, customer lifetime value (CLV), and pricing elasticity.
- Leading customer segmentation and cluster analysis initiatives to identify distinct customer groups and inform Go-to-Market (GTM) strategy and personalization efforts.
- Building and deploying interactive data applications (e.g., dashboards, prediction interfaces) using tools like Streamlit, Dash, or Gradio to operationalize model insights and enable self-service consumption by business teams.
- Exploring and experimenting with Natural Language Processing (NLP) and Large Language Models (LLMs) to analyze unstructured data (e.g., support tickets, sales notes, marketing copy) and derive actionable insights.
- Collaborating with Data Engineers to ensure robust data pipelines and production-ready deployment of models within a stable MLOps framework.
- Clearly communicating complex analytical findings, model methodologies, and business recommendations to both technical and executive stakeholders.
- Own and lead end-to-end data science projects from problem definition to final deployment and monitoring in an autonomous way.
Other
- Bachelor's degree or higher in a quantitative field (e.g., mathematics, statistics, computer science, economics).
- Experience comes in many forms – skills are transferable, and passion goes a long way.
- Dedication to adding new perspectives to the team and encouragement to apply.
- Ability to work in a fast-paced environment and take ownership of a large, revenue-generating area of the business.
- Commitment to investing in growth through mentorship, coaching, and meaningful professional development.
