Join our global team of experts
At Diconium, you will work on projects that connect software, data, AI, and human expertise to create digital products, services, and operating models that deliver measurable business impact. Together with 2,500 experts across our international teams, you will help build, run, and scale intelligence across the full value chain.
In everything we do, we prioritize people and genuine human connections, ensuring a supportive and inclusive work environment. Our hybrid workplace also provides a high degree of flexibility.
Role / Tasks
Build and evolve causal ML solutions for pricing, in particular price elasticity estimation with Double Machine Learning (DML).
Collaborate with cross-functional teams (data engineering, pricing experts, client stakeholders) and take end-to-end ownership of analyses, translating business questions from our clients into well-identified ML/AI solutions.
Own periodic model recalibrations across markets and brands: rerun pipelines on new data windows, compare results against previous runs, and explain results.
Extract valuable information from large, heterogeneous and often imperfect data sources, and detect data-quality problems.
Evaluate model performance and iteratively improve accuracy and efficiency.
Communicate findings and insights effectively to both technical and non-technical stakeholders.
Contribute to a production-grade, config-driven Python codebase using modern engineering practices (testing, typing, code review, spec-driven development, AI-assisted coding).
Must have skills:
Bachelor's degree or higher in Computer Science, Statistics, Mathematics, Physics, Economics/Econometrics, or a related quantitative field.
At least 4 years of relevant work experience.
Proven experience in machine learning and AI, with a focus on practical applications.
Strong proficiency in Python and the scientific stack (pandas and/or polars, NumPy, scikit-learn, statsmodels, gradient boosting such as XGBoost), writing clean, tested, maintainable code (pytest, type hints, linting).
Strong understanding of statistical methods and concepts.
Experience with time-series and panel data, including seasonality, outliers and structural breaks.
A rigorous, hypothesis-driven approach to analysis: forming and testing hypotheses with reproducible scripts and reporting negative or inconclusive results honestly.
Ability to work independently and collaboratively in a fast-paced environment.
Excellent communication skills with the ability to present complex technical concepts clearly.
Hands-on experience with cloud ML platforms, ideally AWS (SageMaker, S3), and experiment tracking (e.g. MLflow).
Fluent English
Nice to have:
Domain knowledge in pricing, revenue management, or the automotive industry (list vs. transaction prices, discounts and incentives, financing/leasing, model lifecycles).
Experience with price elasticity / demand modelling or causal ML libraries (e.g. EconML, DoubleML).
Experience with model interpretability (SHAP), hyperparameter optimization (Optuna) and data validation (pandera, Pydantic).
Experience with model monitoring, recalibration and data/concept drift analysis.
Experience with data processing in Spark and Databricks.
Experience with data visualization, dashboarding tools or simple frontends for visualization (Streamlit, Dash, Tableau, PowerBI).
Experience deploying ML/AI models into production environments.
Familiarity with Git-based workflows, Poetry, pre-commit, CI, containerization (Docker), and AI coding assistants / spec-driven development.
Ability to write clear technical reports (Markdown, LaTeX).
Previous experience in the consulting/service industry.
WHAT WE HAVE TO OFFER
Discover new skills and improve your strengths, adapt your working day to your personal lifestyle, celebrate community, sustainability and diversity. And sweeten your working life with awesome perks and benefits!
Professional & Personal Growth: Develop yourself both professionally and personally…