Top Machine Learning Jobs in Warsaw

Leads a clinical data science team supporting pharmaceutical development, regulatory submissions, and inspection readiness. Oversees statistical and machine learning analyses using clinical trial, biomarker, real-world, and external data. Sets analytical strategy, ensures traceable and submission-ready datasets and outputs, manages vendors and resources, partners cross-functionally with global regulatory teams, and develops direct reports and broader team capabilities.

Senior Clinical Data Scientist supporting pharmaceutical clinical development through statistical analysis, data interpretation, visualization, and decision-support insights. The role develops analysis-ready datasets and reproducible workflows using clinical trial, biomarker, real-world, and other data sources. It partners with cross-functional study teams, applies AI/ML and automation, ensures regulatory compliance and traceability, reviews analytical deliverables, identifies data risks, and supports standards, process improvements, and junior team development.

Manage a team of machine learning engineers developing credit lending models for Europe and Australia. Set technical strategy, lead advanced ML solution development, partner with product, engineering, analytics, design, and risk teams, and ensure scalable underwriting systems. Provide hands-on technical leadership, clarify complex problems, manage trade-offs, and develop engineering talent through mentorship and feedback.

Analyze business data using statistical, predictive modeling, machine learning, and visualization techniques. Develop, evaluate, and monitor analytics models; identify data sources; test hypotheses; generate actionable insights; and communicate recommendations to stakeholders. Collaborate with technical and functional teams to implement models, assess business value, ensure data accuracy, and improve analytics processes and tools.

Lead software engineering efforts for Samsara’s External Platform EMEA, building scalable distributed systems, APIs, reporting, administration, integrations, and business-critical infrastructure. Lead technical discussions, influence architecture and roadmap, deliver full-stack features, maintain production systems, and support customer-focused iteration. Mentor engineers, hire and develop high-performing teams, and contribute to machine learning-powered products and long-running projects.

Leads clinical data science strategy, delivery, and people development across pharmaceutical clinical development programs. Oversees quantitative analyses using clinical, biomarker, real-world, and high-dimensional data; guides statistical, machine learning, simulation, and visualization methods; and ensures analysis-ready, traceable, reproducible, submission-ready outputs. Partners with clinical, regulatory, data management, pharmacovigilance, programming, technology, and external teams. Supports health authority interactions, regulatory submissions, inspection readiness, resource planning, risk mitigation, standards, automation, and capability building.

Manages clinical data science activities supporting pharmaceutical development studies. Performs exploratory analyses, visualization, quantitative assessments, data review, and interpretation across clinical, biomarker, real-world, and other data sources. Develops analysis-ready datasets, reproducible workflows, specifications, and submission-ready deliverables using statistical, machine learning, automation, and advanced analytics methods. Collaborates with cross-functional study teams, communicates data and analytical risks, and supports regulatory-compliant, traceable, high-quality decision-making.

Assess and manage cybersecurity risks across AI systems, models, data pipelines, and supporting environments. Conduct AI security assessments, architecture reviews, threat modeling, and risk assessments; define lifecycle controls, standards, and guardrails; and support governance, monitoring, incident response, and remediation. The consultant will advise financial services clients on secure AI adoption while collaborating with security, data, engineering, architecture, risk, and business stakeholders. The role requires a one-year relocation to Qatar.

Develops and governs enterprise data architecture, including conceptual, logical, and physical data models; master and reference data standards; metadata, lineage, data flows, and data quality controls. The role leads architecture designs across integrated technologies, documents current and future-state landscapes, resolves cross-functional data issues, assesses risks and trade-offs, and aligns programs with enterprise data principles and business objectives.

Paid, onsite engineering internship supporting real satellite, earth observation, sensor technology, and analytics projects. Interns join multidisciplinary hardware, software, or operations teams and apply programming, AI, data science, testing, debugging, documentation, and AI-assisted development skills. The flexible 3–6 month program is based in Finland, Spain, or Poland and includes collaboration, technical problem-solving, presentations, housing, transportation, and potential visa support.

Lead clinical data science and statistical analytics across pharmaceutical development studies. Design analyses using clinical, biomarker, real-world, and external data; oversee partners; define regulated, submission-ready data flows; apply statistics, machine learning, simulation, and visualization; manage analytical risks; mentor colleagues; and advance reproducible, automated, AI-enabled clinical data science practices.

Own complex financial provision calculations, including inventory obsolescence and accounting estimates, while supporting close, reconciliations, governance, and audits. Build databases, automated models, macros, and AI-enabled finance solutions using tools such as Excel, SQL, Python, and Power Automate. Partner with IT and digital teams to improve accounting workflows, investigate reconciliation issues, standardize processes, and eliminate recurring problems.

Lead regulatory engineering projects for Global Banking & Markets, designing, developing, testing, and migrating secure, scalable distributed systems to cloud-native platforms. Responsibilities include technical leadership, event-processing architecture, stakeholder management, compliance with engineering practices, performance monitoring, and implementation of AI-enabled workflows. The role requires expertise in real-time systems, cloud architecture, containerization, NoSQL technologies, production software development, and emerging AI techniques including generative interfaces, RAG, fine-tuning, and multi-agent systems.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Lead strategic Data and AI programs across global, cross-functional teams. Responsibilities include planning and delivery, scope and milestone definition, resource and risk management, stakeholder alignment, governance, performance metrics, budgets, dependencies, and change enablement. The role requires experience managing large-scale technical or digital transformation initiatives and Data, Analytics, or AI/ML operations in enterprise environments.

Develop and evolve ABBYY Vantage cloud-native services for document AI, including OCR pipelines, AI skill workflows, APIs, cloud infrastructure, and analytics. Design, implement, test, deploy, and monitor reliable software; participate in code reviews; resolve production incidents; improve tooling and delivery workflows; and collaborate with product, UX, ML, and engineering teams. The role requires deep C++ expertise, experience with C# .NET, Docker, CI/CD, cloud platforms, Kubernetes, databases, and distributed systems.

Lead architecture and delivery of complex capabilities for ABBYY’s cloud-native Document AI platform. Responsibilities include setting engineering standards, improving service reliability and observability, leading incident response, influencing cross-functional technical roadmaps, mentoring senior and principal engineers, and evaluating technologies including GenAI and cloud-native tooling. The role requires deep C++ expertise alongside C#/.NET, microservices, Kubernetes, Azure, CI/CD, distributed systems, and enterprise-scale software engineering.

Own the roadmap and delivery of data products supporting credit, BNPL, underwriting, fraud, acquiring, and payments decisions. Translate business, risk, and ML needs into buildable specifications, align Data Engineering and ML teams, define outcome metrics, and lead up to three Data Consultants. Establish AI-first, agentic ways of working with appropriate guardrails, evaluations, human oversight, and cost controls. Partner across technical, commercial, and risk stakeholders while managing capacity and priorities across multiple product lines.

Own end-to-end data science projects from scoping through production deployment. Build production-quality Python and PySpark machine learning solutions on Databricks, using entity resolution, record linkage, embeddings, and LLM-augmented pipelines. Apply DataOps and MLOps practices, collaborate with product and engineering, document technical decisions, develop reusable tools, conduct code reviews, and mentor junior data scientists.

Lead the technical strategy and development of production-grade machine learning systems for international credit lending decisions. Build and evaluate large-scale real-time models, collaborate with engineering, product, ML platform, and risk leaders, establish engineering and operational standards, and mentor machine learning engineers while supporting Affirm’s global expansion.

Build and lead InPost’s ML and MLOps function from the ground up. Own the ML platform, model-development standards, validation, deployment, monitoring, retraining, agentic systems, feature-store integration, responsible AI, and regulatory governance. Lead credit and decisioning models with measurable financial outcomes, manage budgets and hiring, and establish reliable, explainable, production-ready AI systems in a regulated environment.

Drive direct and indirect revenue growth across SMB accounts by managing assigned sales opportunities, conducting high-volume outbound outreach, developing sales strategies, and generating monthly recurring revenue. Document activities and forecasts in Salesloft and Salesforce, qualify opportunities, collaborate with Revenue Operations and sales teams, analyze market trends and competition, build telematics expertise, and contribute to strategic revenue planning.

Leads AI platform experience and enterprise adoption by creating reusable blueprints, starter kits, reference architectures, and developer interfaces. Provides technical consulting, evaluates use cases, mentors engineering communities, and translates developer feedback into platform roadmaps. Establishes software delivery standards incorporating responsible AI, security, compliance, and GxP validation. Partners with product, security, quality, and business stakeholders to promote AI offerings and improve platform adoption.

Analyzes data from multiple sources to identify trends, answer business questions, and provide actionable recommendations. Presents insights clearly to stakeholders, monitors recommendation outcomes, and suggests adjustments. Collaborates with business partners while ensuring data acquisition, sharing, and analysis comply with company standards. The role requires SQL, Python, Microsoft Suites, and strong analytical and communication skills, with preferred experience in cloud platforms, Alteryx, generative AI, banking, advanced analytics, and visualization.

Advise city governments across the Americas on designing, prototyping, and launching digital services. Guide system and data architecture, DevOps, infrastructure, AI and machine learning decisions, rapid prototyping, and end-to-end product delivery. Work hands-on with multidisciplinary teams while coaching clients and influencing technical decisions without direct authority. The role requires senior technical leadership, strong communication, and professional English plus Spanish and/or Portuguese.