Principal Data Scientist - For German Resident Only

McKinsey & Company; Düsseldorf; Vor Ort; EUR 90.000 - 120.000; Vollzeit

Zusammenfassung

A leading consulting firm is seeking a Principal Data Scientist to drive GenAI and ML-powered risk solutions. Candidates should possess significant experience in Python and SQL, along with expertise in MLOps and GenAI implementations. Responsibilities include overseeing the development of models, ensuring robust governance, and collaborating cross-functionally with key stakeholders. This role offers a comprehensive benefits package and opportunities for continuous learning and professional development.

Leistungen

Continuous learning opportunities

Exceptional benefits package

Global community exposure

Qualifikationen

• 5-10 years of experience in data roles (data engineer/scientist/software developer).
• Deep expertise in Python (pandas/polars, NumPy, scikit-learn) and SQL.
• Practical GenAI experience with RAG systems and embeddings.

Aufgaben

• Lead design, delivery, and governance of GenAI- and ML-powered risk solutions.
• Own end-to-end delivery of GenAI in risk from concept to production.
• Develop and ship models and services hands-on in Python and SQL.

Kenntnisse

Python

SQL

Data Engineering

MLOps

GenAI

Statistical Methods

Ausbildung

Bachelor's degree in Computer Science or related field

Tools

GitHub

Snowflake

dbt

Docker

Kubernetes

Jobbeschreibung

You are someone who thrives in a high-performance environment, bringing a growth mindset and entrepreneurial spirit to tackle meaningful challenges that have a real impact.

Ist dies der nächste Schritt in Ihrer Karriere Finden Sie heraus, ob Sie der richtige Kandidat sind, indem Sie die vollständige Übersicht unten lesen.

In return for your drive, determination, and curiosity, we’ll provide the resources, mentorship, and opportunities to help you quickly broaden your expertise, grow into a well-rounded professional, and contribute to work that truly makes a difference.

When you join us, you will have :

• Continuous learning : Our learning and apprenticeship culture, backed by structured programs, is all about helping you grow while creating an environment where feedback is clear, actionable, and focused on your development. The real magic happens when you take the input from others to heart and embrace the fast-paced learning experience, owning your journey.
• A voice that matters : From day one, we value your ideas and contributions. You’ll make a tangible impact by offering innovative ideas and practical solutions. We not only encourage diverse perspectives, but they are critical in driving us toward the best possible outcomes.
• Global community : With colleagues across 65+ countries and over 100 different nationalities, our firm’s diversity fuels creativity and helps us come up with the best solutions. Plus, you’ll have the opportunity to learn from exceptional colleagues with diverse backgrounds and experiences.
• Exceptional benefits : On top of a competitive salary (based on your location, experience, and skills), we provide a comprehensive benefits package to enable holistic well‑being for you and your family.

As a Principal Data Scientist, you will lead the design, delivery, and governance of GenAI- and ML-powered risk solutions that mitigate risks, sharpen controls, and make the risk function markedly more efficient.

You will own the end-to-end delivery of GenAI in risk from concept to production, and architect and build retrieval-augmented generation (RAG) pipelines and evaluators (prompt design, grounding data curation, guardrails, red‑teaming, offline / online evals), ensuring factuality, privacy, and cost / performance balance.

You will develop and ship models and services hands‑on in Python (data prep, feature engineering, training / inference, APIs); write high‑quality, tested code and drive code reviews in GitHub, query and transform data with SQL; partner closely with data engineering to model lineage and build reliable pipelines using dbt on Snowflake (or similar modern data stack).

You will apply traditional ML where appropriate (classification, anomaly detection, NLP, forecasting) and integrate with GenAI approaches; choose the simplest method that meets risk and performance requirements.

Additionally, you will produce using MLOps / LLMOps best practices : CI / CD, containers, orchestration, feature / embedding stores, vector search, monitoring (data drift, model decay, hallucination / factuality) and embed robust risk & model governance : documentation, explainability, validation / testing standards.

You will lead cross-functional partnerships with Risk / Compliance, Legal, Security, Product, and Engineering; translate risk policy into technical requirements and communicate trade-offs to senior stakeholders.

You will mentor and upskill data scientists / analysts; establish reusable components, templates, and internal best practices for GenAI in risk.

Your work will materially improve how risk is identified, assessed, and mitigated—shortening investigation cycles, reducing false positives, automating manual controls, and strengthening regulatory compliance while enabling the business to move faster with confidence.

You will be based in Europe as part of our Risk Technology & AI team. This team partners with risk and business leaders to modernize controls, streamline operations, and unlock value from data and AI across the enterprise.

• Bachelor’s degree or equivalent work experience required (e.g. Computer Science)
• 5-10 years of experience working in data roles (i.e., data engineer / scientist / software developer) in a professional environment
• Deep expertise in Python (pandas / polars, NumPy, scikit‑learn a plus) and strong SQL; hands‑on experience with dbt and Snowflake preferred
• Fluency with GitHub and DevOps / infra concepts (CI / CD, Docker / Kubernetes, secrets management, observability)
• Practical GenAI experience : building RAG systems, embeddings, evaluation harnesses, guardrails / content filters; familiarity with vector databases and prompt- / system-design patterns
• Solid grounding in traditional ML and statistical methods; ability to decide when classical approaches beat LLMs
• Understanding of model governance and risk management standards; experience building auditable, production‑grade systems in regulated or risk‑sensitive environments
• Strong product thinking and stakeholder management; proven track record shipping measurable value in production