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# Lead Data Scientist

- **Company:** COMPETERA INC
- **Location:** San Mateo, CA, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Code Review, Information Engineering, Data Integration, Python (Programming Language), NumPy, Data Mesh, Tensorflow, Standard Sql, Pytorch, Delivery Pipeline, Apache Spark, Keras, Pandas, Scikit Learn, Information Technology, Data Analytics, Databricks
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25303_81641a7db17c4ff9bbba1ba0dc33aa1b

## About the Role

* Minimum 5+ years of experience in Data Science or a related field with a focus on delivering value to production.
 * Strong Python and SQL skills; ability to write modular, readable code for experiments and prototypes.
 * Familiarity with Databricks and Apache Spark.
 * General familiarity with Data Mesh approach and ability to leverage best practices with data engineers.
 * Solid mathematical background, preferably in a Computer Science-related field.
 * Proficiency with scientific Python toolkit: NumPy, pandas, scikit-learn, and either Keras/TensorFlow or PyTorch.
 * Deep understanding of statistical testing methodologies, especially A&B testing design.
 * Familiarity with Time Series Forecasting approaches.
 * At least 3 years working with tabular and mixed (multimodal) data.
 * Expertise in Causal Inference; background in Ecommerce/Retail is a strong plus.
 * Upper-intermediate or higher English and strong public speaking skills.
 * Focus on value delivered to users and the business, not only mathematical model behavior.
 * Ability to translate complex technical concepts into business language for stakeholders.
 * Ability to communicate professionally with PhD-level customer scientists during sales, pilot, and rollout.
 * Own data requirements and build efficient processes for integration and data engineering, including data integration and validation requirements.
 * Willingness to experiment, pivot, and make data-driven decisions in a dynamic environment.
 * Proactively contribute ideas to the Product Backlog based on technical knowledge and capabilities.
 * Entrepreneurial mindset and curiosity with a drive to continuously learn.

## Description

* Own and maintain a clear Data Science vision and strategy for the organization.
 * Shape the technical roadmap based on business needs, team capacity, and long-term strategy.
 * Gradually scale the data science function as the company grows.
 * Define the hiring profile, lead and mentor a cross-functional team of Data Scientists, and grow an R&D culture focused on scientific rigor and measurable business impact.
 * Manage resources across both Delivery (S&D) and Research (R&D) squads.
 * Act as the main bridge between R&D and S&D by translating business requirements into research tasks.
 * Help teams convert complex prototypes into stable, production-ready solutions.
 * Architect client solutions by selecting appropriate models and assortment strategies to solve specific business problems using the existing stack.
 * Serve as a quality gatekeeper: conduct code reviews, verify A&B test designs, and ensure the team meets Definition of Done (DoD).
 * Drive key DS metrics including research-to-production time, experiment velocity, and overall quality of price recommendations.
 * Communicate clearly with product managers, engineers, and occasionally clients to explain trade-offs, present results, and align on next steps.
 * Represent COMPETERA's technology expertise to investors and partners during due diligence by articulating DS strategy, algorithm defensibility, and the technical roadmap.
 * Engage directly with customers' Data Science and Analytics teams, including PhD-level experts, by explaining methodology, model assumptions, and performance results, and handling technical questions.
 * Present complex model results and performance metrics to key stakeholders in a clear, concise, business-focused way.
 * Support sales and pre-sales with technical narratives demonstrating value in the pricing domain.
 * Stay hands-on as needed, including designing new experiments, addressing critical issues in the delivery pipeline, and prototyping new ideas., * Python
 * SQL
 * Databricks
 * Apache Spark
 * NumPy
 * pandas
 * scikit-learn
 * Keras
 * TensorFlow
 * PyTorch