Data Scientist

About The Role

We are seeking a versatile Data Scientist to join the Functional Analytics team. The core mission of our team is to design and develop new functional analytic capabilities or “engines” that integrate directly into Anaplan Applications to solve complex enterprise business problems.

In this role, you will develop best-in-class ML models and pipelines to drive automated, actionable insights at scale. Beyond core development, you will act as a key technical partner for our customers. You will play a highly visible role in driving customer onboardings, getting hands-on with client data to customize and fine-tune our ML engines so they seamlessly adapt to complex business realities and specific customer needs.

Your Impact

Customer Onboarding & Engine Customization: Lead the technical deployment of our engines for new clients. Partner closely with customers, Customer Success, Product, and Engineering to adapt and customize ML models, ensuring they accurately reflect client-specific business logic, operational guardrails, and unique data nuances.

Analytic Engine Development: Design and develop scalable functional analytic capabilities that seamlessly integrate into Anaplan Applications, translating raw customer data into automated, high-value business insights.

Advanced Modeling & Timeseries Forecasting: Design, train, and implement robust Machine Learning (LightGBM, XGBoost) and Deep Learning (PyTorch) models specifically focused on complex demand sensing and resolving cold-start forecasting problems.

Feature Engineering Pipelines: Build and optimize scalable data pipelines leveraging a hybrid SQL/Python architecture (dbt, Snowflake) to extract rich, localized temporal signals from high-volume transactional retail data.

End-to-End Deployment (MLOps): Partner with our engineers to transition your models from prototype into scalable, monitored, and reliable production pipelines using tools like MLflow and Optuna.

Your Skills

Strong hands-on professional experience in Data Science, successfully building and deploying systems from prototype to production in a fast-paced environment.

Strong predictive modeling foundation: Extensive experience with traditional ML algorithms, Deep Learning frameworks, and complex time-series forecasting.

Experience developing production-grade Python code: High proficiency in Python, Pandas, and modern software practices (testing, clean code, code review, CI/CD).

Data & Pipeline Expertise: Hands-on experience optimizing data models and access patterns for modern data warehouses (Snowflake, Databricks) utilizing SQL and transformation tools like dbt.

Cloud & Data Infrastructure: Experience working with scalable cloud infrastructure (AWS, GCP, or Azure), and handling columnar data formats like Parquet.

MLOps Exposure: Experience with tools and frameworks for training, tracking, deploying, and monitoring models in production (e.g., MLflow, Optuna, Prefect, or Airflow).

Desirable

Background in supply chain forecasting, enterprise AI, or retail analytics

M.S. or Ph.D. in Computer Science, Artificial Intelligence, Statistics, Data Science, or a related quantitative field.