Senior Data Engineer - Recommendations

Mentimeter

Mentimeter is an engagement tool with a clear goal in mind. To turn presentations into conversations. Through real-time interactivity and clear visualizations, we get people to participate, engage and become more productive. Transforming all those passive meetings, airless classrooms and drawn out trainings into valuable and memorable moments. We truly believe that you achieve the best results by doing things together. And that successful leaders need to adopt a curious and collaborative mindset in order to get there. So with you at Mentimeter, you’ll be a big part of the ambition to help over 1 billion people listen, learn and work better together. SUMMER HIRING NOTICE: DUE TO SUMMER VACATIONS, WE WILL BEGIN THE REVIEWING AND INTERVIEWING PROCESS FOR THIS ROLE FROM AUGUST. HOWEVER, WE ENCOURAGE YOU TO SUBMIT YOUR APPLICATION AS SOON AS POSSIBLE. ABOUT THE ROLE AI is changing how people create and consume content, and the Insights product area is where Mentimeter turns that shift into an advantage rather than something we retrofit later. It's the area that decides what every one of our users sees, learns and does next, to turn data into something valuable enough to come back for, and in turn drive retention and expansion for our business. As Senior Product Manager, Data / ML, you'll own the data and ML depth of Insights: the intelligence that turns every interaction into sharper results, insights and recommendations, pulls in context from inside and outside Mentimeter, and gets better the more it's used. With 300 million users' worth of interaction data behind it, the mandate is to turn that into a defensible asset. The data and ML is the means; a system that drives measurable outcomes for users and the business is the end. This is a senior, hands-on role with a clear mandate. You'll report to the Product Director of Insights: the Director owns the area's place in company strategy and its org shape, while you own the data and ML depth that delivers it. You'll work alongside the other product managers in the area, our Staff Engineers, our Science Lead, the Office of the CTO, and our data platform teams. The exact shape of the teams is still forming, and you'll help shape it. In your first year, success looks like a clear direction the area is bought into, and the first solid evidence that the system is compounding. WHAT YOU'LL DO OWN THE DATA AND ML DIRECTION * Set the data and ML direction for the area and get alignment behind it across product, engineering and leadership. * Turn raw interaction data into immediately useful, actionable insight for users, at the individual level and aggregated up to the organization, measured by whether people come back and act on it. * Keep recommendations grounded in science, not just patterns in the data, working closely with our Science Lead to keep what we surface credible and defensible. BUILD THE INTELLIGENCE PLATFORM, FOR UI AND AGENTS * Own the platform that generates insights and recommendations: opinionated, grounded in our thought leadership, and built so the rest of the suite can build on top of it. * Make that intelligence usable two ways at once, surfaced directly in Mentimeter's UI and exposed so our AI companion and external agents can act on it, with a clean separation between the logic and context layer and the experience layer. * Build the ranking and timing that gets the right insight to the right user at the right moment, getting sharper from what lands. * Partner with Staff Engineers on the architecture and technical bets: you bring the product judgment and the outcomes the system must serve, they own the technical design. MAKE IT LEARN, AND PROVE IT * Define the measurement and experimentation standards the area scales by, so we only scale what shows durable lift against a strong baseline. * Show, with evidence, how the system's insights and recommendations get sharper over time as it learns from feedback, for example through reinforcement learning. * Own how we ship AI well: anticipate failure modes, set quality bars, guard against feedback loops that reinforce errors, and roll back cleanly when the model is wrong. * Make pragmatic calls on when to reach for ML, GenAI or something simpler, proving value before scaling. WHAT WE'RE LOOKING FOR AN EXPERIENCED DATA / AI PRODUCT LEADER * Several years leading data-driven or ML-backed products end to end, from discovery through rollout, ideally in B2B SaaS. * Experience with recommendation engines or similar ranking and personalization systems is highly relevant, and familiarity with AI ecosystems and integration patterns such as MCP is a bonus. YOU BUILD THE DATA SYSTEM, NOT JUST THE SURFACE * You've shipped data or AI products where the core value is the system itself, and you're comfortable reasoning about pipelines, models, and how data compounds over time. * You've led shared-capability or platform work where the real win was adoption and alignment across teams, not just shipping features. COMMERCIALLY MINDED AND OUTCOME-DRIVEN * You connect product decisions to business outcomes like retention, expansion and durable engagement, and treat data and ML as how you get there, not the point in itself. * You have a bias for the simplest solution that works, you resist over-engineering, and you have good judgment on when advanced ML or GenAI is worth the trade-off. RIGOROUS AND AI-NATIVE * Strong on experimentation, with real fluency in A/B testing, incrementality and causal inference. * Fluent in shipping AI responsibly and well: eval sets, failure modes, and a clear sense of what "good enough" looks like before something goes live. A LEADER WHO MULTIPLIES OTHERS * You lead through influence and coaching, make progress in ambiguity through iterative learning and clear decision points, and bring out the best in the people around you. Not sure you meet 100% of our qualifications? Please apply anyway and let us know why you'd enjoy working in this role at Mentimeter. PRACTICAL DETAILS * This role is based in our Stockholm office, primarily onsite with the option to work from home twice per week. * Swedish is not required. Daily work is carried out in English, and the Mentimeter team currently boasts 50+ nationalities. * A cover letter is not required as part of your application. Compensation At Mentimeter, we believe in fair and transparent compensation that grows with you. The salary range for this role is SEK 70,000–SEK 91,000 per month. It’s intentionally broad to reflect the different stages of growth within the role: from early development to deep expertise and meaningful impact. Where an individual is placed within the range depends on factors such as relevant experience, demonstrated skills, and alignment with the role’s requirements. We’re committed to supporting your growth. You’ll have ongoing development conversations with your manager, and your salary will evolve as you build skills and contribute to our mission. In addition to your monthly salary, we offer a comprehensive benefits package—learn more about it here: https://www.mentimeter.com/benefits/stockholm. What Mentimeter can offer At Mentimeter we can offer a diverse and inclusive work environment supported by smart and driven colleagues. We believe in continuous professional development for all of our colleagues and therefore offer access to a leadership program (including external personal coach) and relevant education to ensure that we continue to be state-of-the-art when it comes to innovating and building Mentimeter. Your place will be in a growing company with lots of career opportunities, working on a beloved product used by more than 300 million people. It’s not all about work though, we also offer a very healthy view on work-life balance. All of this comes attached with a competitive compensation and benefits package, including pension contributions. Learn more about our benefits by visiting our Benefits & Perks page AI and Hiring at Mentimeter At Mentimeter, we believe AI helps us work smarter - but it never replaces the human assessment, curiosity, and personal connection that define our culture and our hiring. We use AI as a sparring partner: to bounce ideas, bring new perspectives, support structure, and make our work more efficient. But the meaning, decisions, and interactions always come from people. * AI does not screen or decide on candidates. *  There is no automated filtering, ranking, or decision-making in our recruitment process. Every application is reviewed by a person. * Hiring teams may use AI to support their work - for example, to structure notes, prepare interview questions, or organize their thinking. AI strengthens our work, but it does not define it. At Mentimeter, we’re not building an AI-driven hiring process - we’re building a people-first culture, where technology helps us listen, learn, and grow together. Culture at Mentimeter At Mentimeter we believe in giving everyone a voice - regardless of who you are. So we build a platform that does just that. Our platform is not only our product but also our organization. A platform where people feel safe, where differences are embraced, a place where you can have fun. We strongly encourage applicants who are people of color, LGBTQ+, women, people with disabilities, and/or formerly incarcerated people, and a college degree is not strictly required. In order to give everyone a voice, we need to be as diverse as our users. Learn more about our culture by visiting our Culture page. Review our Privacy Policy for more information.

Klarna Bank AB

What you will do Perform independent end-to-end validation of fraud detection ML models, including conceptual soundness, data integrity, feature engineering, model development, deployment design, and monitoring frameworks. Develop challenger models. Review and challenge first-line fraud model methodologies, assumptions, and implementation choices (e.g., scikit-learn, LightGBM, graph models, anonaly detection techniques, GenAI components). Build and deploy agentic AI tools to support model validation workflows — automating review of model documentation and code, surfacing risks and inconsistencies. Assess model performance using appropriate fraud metrics (e.g., precision/recall, ROC-AUC, PR-AUC, cost-sensitive metrics, fraud rate capture, business impact trade-offs). Evaluate model stability, drift detection, retraining strategies, and production monitoring practices. Independently replicate model results where necessary and conduct challenger analyses to assess model robustness and limitations. Review large-scale transaction datasets and feature pipelines (e.g., >100M transactions, hundreds of features) to assess data representativeness, leakage risks, and bias. Evaluate model governance documentation, explainability approaches, and transparency — including regulatory compliance related to model risk, fairness, and data privacy. Validate new technologies applied in fraud detection, such as Graph Networks, Behavioral Biometrics, Anomaly Detection, and GenAI-based systems. Assess controls around CI/CD pipelines, deployment processes (e.g., Docker, Jenkins), and cloud environments (e.g., AWS SageMaker, S3, Athena, Lambda). Develop and maintain validation frameworks, testing standards, and model performance monitoring tools (e.g., SQL, PySpark, Python-based validation libraries). Collaborate closely with first-line fraud data scientists, ML engineers, product, and business stakeholders to ensure transparent communication of model risks and validation findings. Provide actionable recommendations and formally document validation outcomes in line with internal model governance standards and external regulatory expectations. Stay up to date with evolving fraud typologies, emerging ML/AI techniques, and regulatory developments in model risk management. Who you are Advanced degree (Master’s or PhD) in a quantitative field such as Data Science, Statistics, Mathematics, Computer Science, Physics, or Engineering. 3+ years of hands-on experience in fraud-related modeling (e.g., transaction fraud, account takeover, identity fraud, payments fraud etc). Strong expertise in machine learning methods used in fraud detection, including tree-based models (e.g., LightGBM), anomaly detection, graph/network models, and advanced ML techniques. Deep understanding of the end-to-end ML lifecycle — from conceptual design and feature engineering to production deployment and monitoring — with the ability to critically challenge each stage. Strong programming skills in Python and SQL; experience with PySpark/Spark and large-scale data processing. Experience building agentic AI workflows. Familiarity with cloud-based ML platforms (e.g., AWS SageMaker, Lambda, S3, Athena) and production deployment workflows. Strong knowledge of model validation principles, model risk governance frameworks, and regulatory expectations. Experience assessing model bias, fairness, explainability, and privacy risks. Excellent analytical thinking and structured problem-solving skills, with the ability to assess complex models and clearly articulate risks and limitations. Strong communication skills, capable of translating technical findings into clear, actionable insights for senior stakeholders and non-technical audiences. Ability to work independently while constructively challenging first-line teams in a collaborative manner. Awesome to have Experience in BNPL, credit cards, payments, or other transaction-heavy financial products. Experience validating models in highly regulated environments. Experience mentoring junior validators or leading validation reviews. Exposure to inference of rejected transactions and understanding of fraud/credit overlap. Familiarity with AI governance frameworks and emerging AI regulatory requirements.

Tyche Consulting AB

Location: Stockholm, Sweden (Flexible/Hybrid) Employment Type: Full-time Language: English About Us We are a fast-growing IT consultancy dedicated to delivering cutting-edge expertise to industry-leading clients. Our mission is simple: empower organizations to build scalable, data-driven products that elevate user experience, optimize operations, and unlock business performance through modern AI and advanced analytics. As we scale, we are looking for a seasoned Senior AI & Data Scientist to join our elite team of experts and help shape the future of our AI offerings. The Role & Responsibilities As a Senior AI & Data Scientist, you will own the full AI lifecycle—from initial research and development to deployment and production monitoring. You will collaborate closely with product managers, engineers, and business stakeholders to turn complex data into competitive advantages. Key Responsibilities: Model Development: Design and deploy advanced Machine Learning models for business optimization, predictive analytics (forecasting/classification), and recommendation systems. Generative AI: Build next-generation, LLM-based applications utilizing RAG and Agentic AI architectures. Data & ML Ops: Design scalable ETL/feature engineering pipelines and build production-ready ML pipelines with CI/CD workflows. Deployment: Deploy and manage ML models efficiently using Docker and Kubernetes. Domain Solutions: Develop specialized AI solutions for fraud detection, risk analysis, and customer personalization. Strategic Impact: Analyze large-scale datasets to generate actionable business insights and actively contribute to our clients' long-term AI strategies. Required Qualifications To thrive in this role, you should bring a strong blend of theoretical knowledge and hands-on production experience. Education: Degree in Computer Science, Computer Engineering, Data Science, or a related quantitative field. Experience: 6–8+ years of professional experience in Data Science, Machine Learning, or AI Engineering. Core Tech: Exceptional programming skills in Python and strong proficiency in SQL. ML/Stats: Deep understanding of Machine Learning algorithms, statistical modeling, and predictive analytics. Cloud & Infrastructure: Hands-on experience with cloud platforms (GCP, AWS, or Azure) alongside Docker and Kubernetes. Data Engineering: Experience building production-grade ML pipelines and working with distributed data platforms like Databricks. Best Practices: Strong version control habits (Git) and experience with CI/CD pipelines. Soft Skills: Excellent English communication skills, with the ability to translate complex technical concepts for business stakeholders. Preferred Qualifications (Nice to Have) Experience with any of the following tools and concepts is considered a distinct advantage: GenAI Stack: LangChain, LlamaIndex, RAG architectures, and Agentic AI. MLOps Tools: MLflow, Kubeflow, Airflow, and Feature Stores. Data Tools: DBT, BigQuery, and real-time data processing frameworks. Specialized Analytics: A/B testing, customer analytics, and fraud detection. What We Offer Compensation: Salary plus a very attractive bonus package based on performance. Flexibility: A flexible working environment that supports a healthy work-life balance. Impact: A direct opportunity to shape the AI strategy for major global brands. Culture: A vibrant, collaborative, and international team environment. Growth: Structured professional development opportunities and clear paths for long-term career progression.