Lead Data Scientist

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.
Stay hands-on: dive into code and architecture when needed - whether it's designing a new experiment, fixing a critical issue in the delivery pipeline, or prototyping a new idea.

What you 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, capable of writing modular and readable code for experiments and prototypes.
Familiarity Databricks data platform and Apache Spark.
General familiarity with Data Mesh approach and ability to leverage its best practices in collaboration with data engineers.
Solid mathematical background, preferably in a Computer Science-related field.
Proficiency in the scientific Python toolkit, including NumPy, pandas, scikit-learn, and either 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.
Expertise in Causal Inference and background in Ecommerce/Retail are strong pluses.
Upper-intermediate or higher level of English and strong public speaking skills.

Soft skills
You care not only about how the model works mathematically but what value it brings to the user and the business.
Able to translate complex technical concepts into simple business language for stakeholders.
Able to professionally and clearly communicate with PhD scientists on customer side during the sales process, pilot and rollout.
Able to own data requirements and build efficient processes to support integration and data engineering with outlining data integration and validation requirements.
Ready to experiment, pivot, and make data-driven decisions in a dynamic business environment.
Proactively contribute ideas to the Product Backlog with ideas based on technical knowledge and capabilities.
Be able to think outside of the box and find simple and elegant solutions to complex problems, including technical, product and processes-related ones.
Curiosity and a drive to continuously learn within the domain.
Have a truly entrepreneurial mindset.
You’re gonna love it, and here’s why:
Rich innovative software stack, freedom to choose the best suitable technologies.
Remote-first ideology: freedom to operate from the home office or any suitable coworking.
Flexible working hours (we start from 8 to 11 am) and no time tracking systems on.
Regular performance and compensation reviews.
Recurrent 1-1s and measurable OKRs.
In-depth onboarding with a clear success track.
Competera covers 70% of your training/course fee.
20 vacation days, 15 days off, and up to one week of paid Christmas holidays.
20 business days of sick leave.
We reimburse the cost of coworking.
Drive innovations with us. Be a Competerian.