Lead Data Scientist
Lead Data Scientist at Competera, a company building AI-driven price optimization for retailers. You own the DS strategy and roadmap, lead and mentor the data science team across R&D and delivery squads, architect client pricing solutions, and stay hands-on with Python, SQL, Databricks/Spark and the scientific Python ML stack.
- Compensation
- Not disclosed
- Location
- Europe
- Work setup
- Remote
- Seniority
- Lead
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Data science
- Posted
Skills
Must have
- 5+ years of experience in Data Science or a related field with a focus on delivering value to production
- Strong Python and SQL skills, writing modular and readable code for experiments and prototypes
- Familiarity with Databricks data platform and Apache Spark
- General familiarity with Data Mesh approach and its best practices
- Solid mathematical background, preferably in a Computer Science-related field
- Proficiency in scientific Python toolkit: NumPy, pandas, scikit-learn, and Keras/TensorFlow or PyTorch
- Deep understanding of statistical testing methodologies, specifically A/B testing design
- Familiarity with Time Series Forecasting approaches
- At least 3 years of experience working with tabular and mixed (multimodal) data
- Upper-intermediate or higher level of English and strong public speaking skills
Nice to have
- Expertise in Causal Inference
- Background in Ecommerce/Retail
Full description
In Competera, we are building a place where optimal pricing decisions can be made easily. We believe that AI technologies will soon drive all challenging decisions and are capable of helping humans be better.
We are now looking for a hands-on Leader who can bridge the gap between advanced research and real-world product delivery. You will be the technical anchor for two streams: S&D (Service & Delivery), ensuring our clients get robust, profitable pricing recommendations, and R&D (Research & Development), guiding the strategy for next-gen algorithms.
What you will do
Have clear vision and own the strategy of Data Science in the organization.
Shape the technical roadmap based on business needs, team capacity, and long-term strategy.
Gradually scale data science function with company growth.
Define the hiring profile, lead and mentor a cross-functional team of Data Scientists, and cultivate a high-performing 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: translate business requirements into research tasks, and help the team adapt complex prototypes into stable, production-ready solutions.
Architect client solutions by selecting the right models and assortment strategies to solve specific business problems using our existing stack.
Serve as the quality gatekeeper for the team, conducting code reviews, verifying A/B test designs, and ensuring the team hits their Definition of Done (DoD).
Drive key DS metrics such as research-to-production time, experiment velocity, and the overall quality of price recommendations.
Communicate clearly with product managers, engineers, and occasionally clients to explain DS trade-offs, present results, and align on next steps.
Confidently represent the company's technological expertise to investors and partners. You will clearly articulate the Data Science strategy, the core algorithms' defensibility, and the technical roadmap during high-stakes due diligence processes.
Engage directly with customers' Data Science and Analytics teams (including PhD-level experts). You must be able to confidently explain our methodology, model assumptions, performance results, and address their most technical questions, serving as a critical differentiator during sales and pilot phases.
Present complex model results, performance metrics, and strategic trade-offs to key stakeholders in a clear, concise, and business-focused manner.
Support the sales and pre-sales process by building compelling technical narratives that demonstrate our value proposition and deep expertise in the pricing domain.