Senior Full-Stack ML Engineer (m/w/d) at Cint

Berlin, , Germany -

Full Time

Start Date

Immediate

Expiry Date

21 May, 26

Salary

0.0

Posted On

20 Feb, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Full-Stack Engineering, Python, Spark, Databricks, Data Pipelines, Feature Engineering, Model Monitoring, Exploratory Data Analysis, A/B Testing, Hypothesis Testing, SQL, PyTorch, Scikit-learn, XGBoost, Docker

Industry

Software Development

Description

Company Description Cint is a pioneer in research technology (ResTech). Our customers use the Cint platform to post questions and get answers from real people to build business strategies, confidently publish research, accurately measure the impact of digital advertising, and more. The Cint platform is built on a programmatic marketplace, which is the world’s largest, with nearly 300 million respondents in over 150 countries who consent to sharing their opinions, motivations, and behaviours. We are feeding the world’s curiosity! Job Description We are seeking a Senior Full-Stack Machine Learning Engineer who thrives at the intersection of software engineering and data science. In this role, you will be the bridge between raw data and product impact. You won’t just be training models in a vacuum; you will be architecting the data pipelines that feed them and the production systems that serve them. This is a hybrid role for a builder who thinks like a scientist. You will not only build the engines (ML Engineering) but also act as the navigator (Data Science), using data to tell us where the product should go next. What You'll Do: Key Responsibilities End-to-End ML Lifecycle: Design, develop, and deploy production-grade ML models using Python and Spark. You will own the full cycle from feature engineering to model monitoring. Data Architecture & Pipelines: Build and maintain robust data pipelines within our Databricks environment. Exploratory Data Analysis (EDA) & Discovery: Dive deep into large datasets to uncover hidden patterns, anomalies, and opportunities. You don’t just process data; you interpret what it says about our users. Statistical Rigor & Hypothesis Testing: Design and execute rigorous A/B tests and multivariate experiments. You will be responsible for calculating sample sizes, p-values, and confidence intervals to ensure product changes are statistically significant. Metric Definition: Work with stakeholders to define what "success" looks like. You will translate vague business questions (e.g., "Why is churn increasing?") into measurable data science problems. Predictive Modeling & Insights: Beyond production pipelines, you will create ad-hoc models to forecast business trends and provide actionable insights that influence the product roadmap. Data Storytelling: Communicate complex findings through high-quality visualizations and dashboards (using tools like Tableau, PowerBI, or Databricks SQL). You can tell a "story" with data to convince leadership of a strategic direction. Product Impact: Collaborate with Product Managers to translate business goals into technical ML objectives. You will be responsible for defining and moving key performance indicators (KPIs) through algorithmic improvements. Collaborative Engineering: Work as a peer within the engineering team, applying software best practices (unit testing, code reviews, design docs) to the ML stack. Qualifications Education: A Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related technical field. A strong foundation in algorithms and data structures is non-negotiable. ML Expertise: Proven experience (3+ years) in building and deploying ML models in a production environment. You should be deeply familiar with libraries like PyTorch, Scikit-learn, or XGBoost. Data Stack Mastery: Hands-on experience with Databricks and Data Lake architectures. You should be proficient in PySpark and SQL for large-scale data processing. Software Engineering Mindset: You write "production-ready" code. You are comfortable with Docker, Kubernetes, and modern cloud infrastructure (AWS/Azure/GCP). Location: You are based in or willing to relocate to Berlin. Communication: Fluent in English, with the ability to explain complex technical trade-offs to non-technical stakeholders. Additional Information Our Values Collaboration is our superpower We uncover rich perspectives across the world Success happens together We deliver across borders. Innovation is in our blood We’re pioneers in our industry Our curiosity is insatiable We bring the best ideas to life. We do what we say We’re accountable for our work and actions Excellence comes as standard We’re open, honest and kind, always. We are caring We learn from each other’s experiences Stop and listen; every opinion matters We embrace diversity, equity and inclusion. More About Cint We’re proud to be recognised in Newsweek’s 2025 Global Top 100 Most Loved Workplaces®, reflecting our commitment to a culture of trust, respect, and employee growth. In June 2021, Cint acquired Berlin-based GapFish – the world’s largest ISO certified online panel community in the DACH region – and in January 2022, completed the acquisition of US-based Lucid – a programmatic research technology platform that provides access to first-party survey data in over 110 countries. Cint Group AB (publ), listed on Nasdaq Stockholm, this growth has made Cint a strong global platform with teams across its many global offices, including Stockholm, London, New York, New Orleans, Singapore, Tokyo and Sydney. (www.cint.com) Additionally, in a world of AI, we want our candidates to understand our approach to the use of AI during the interview and hiring process, so we'd appreciate you reading our AI usage guide.

Responsibilities

The role involves owning the end-to-end Machine Learning lifecycle, from designing and deploying production-grade models using Python and Spark, to building and maintaining robust data pipelines within the Databricks environment. Responsibilities also include conducting rigorous statistical analysis, defining success metrics, and creating ad-hoc models to influence the product roadmap.