Top Machine Learning Jobs in Poland

Owns Takeda’s ESG and supplier-risk intelligence capability within Procurement Excellence. The role defines data models, taxonomies, metrics, quality standards, reporting products, and analytics services supporting sustainability, supplier risk, disclosures, benchmarking, and executive decisions. It leads predictive modeling and AI-enabled use cases, partners with enterprise data and digital teams, establishes the capability roadmap, and directs quality standards and deliverables for supporting managers and analysts.

Leads ESG and supplier risk analytics for Global Procurement by building dashboards, reporting solutions, datasets, and intelligence products. Integrates and validates internal and external data, conducts supplier and sustainability analyses, supports predictive analytics and AI initiatives, and communicates actionable insights to leadership. The role also manages reporting governance, data quality, analytics requests, automation, and continuous improvement across procurement stakeholders.

Leads Takeda’s global Procurement Insights & Intelligence function, shaping procurement analytics, market intelligence, data governance, AI-enabled decision support, supplier risk sensing, and forecasting. Partners with Procurement, Finance, DD&T, and business stakeholders to inform sourcing, supplier, investment, and risk decisions. Oversees data providers, analytics adoption, performance measurement, stakeholder communications, and a global team’s development and delivery.

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.

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.

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.

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.

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.

Leads operational excellence across Takeda’s Global Finance Center of Excellence by designing and improving performance, continuity, risk, process documentation, knowledge-sharing, and governance frameworks. Develops Lean Six Sigma and Agile training, mentors improvement projects, coaches teams, manages KPIs, and drives process improvements using data, digital solutions, AI, and machine learning. Requires strong stakeholder management, analytical thinking, business judgment, and experience delivering improvement initiatives in transactional environments.

Leads operational excellence initiatives across Takeda’s Global Finance Center of Excellence. Develops Lean Six Sigma, Agile, and process improvement training; coaches teams on DMAIC projects; manages performance KPIs; improves processes through data, digital solutions, AI, and machine learning; and establishes governance frameworks supporting operational resilience, business continuity, risk management, process documentation, and sustainable performance.

Lead and build the ML and MLOps function from the ground up in a regulated fintech environment. Own the ML platform, production model lifecycle, credit and decisioning models, agentic systems, feature-store integration, responsible AI governance, model-risk validation, monitoring, retraining, and team hiring. Ensure models deliver measurable financial value while meeting regulatory, reliability, cost, latency, explainability, and bias requirements.

Supports the GR&A Data Science team across the modeling lifecycle, including data preparation, analysis, documentation, statistical and machine learning model development, forecasting, decision optimization, and stakeholder presentations. The intern works under senior supervision, writes version-controlled code, and collaborates on well-defined modeling tasks.

Own product and research launch marketing from technical positioning through distribution. Translate AI research, training runs, and benchmarks into blogs, explainers, social content, landing pages, documentation, sales collateral, and campaigns. Engage technical communities, coordinate cross-platform launches, and use analytics to optimize reach, engagement, traffic, and adoption. The role requires strong technical writing, product marketing judgment, rapid execution, and experience communicating AI, ML, or developer tools to developers and enterprise audiences.

Develop and productionize demand forecasting and price elasticity models for automated pricing. Research modeling approaches, validate hypotheses, advise product and business stakeholders, collaborate on reliable implementation, monitor pricing behavior, respond to support cases, and improve system reliability and observability. The role requires quantitative analysis, programming, SQL, basic machine learning, strong communication, and independent contractor operation under a B2B agreement in Poland.

Leads technical direction for machine-learning tools and systems supporting Unreal Engine workflows, game production pipelines, developer productivity, and game subsystems. Manages and develops engineers and researchers, sets technical standards, guides pragmatic ML decisions, and aligns engineering, research, production, QA, and game development stakeholders. The role is based in Warsaw, Poland, with required relocation and hybrid or full-time office work.

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.

Manages customer relationships, onboarding, success plans, recurring business reviews, product adoption, renewals, and expansion opportunities. The role assesses customer needs, coordinates services and training, documents customer health, gathers product feedback, and represents customer priorities internally. It requires German and English fluency, strong communication and presentation skills, disciplined follow-up, flexibility for international customers, and an interest in cybersecurity and networking.

Lead cybersecurity governance, risk, and compliance consulting engagements. Responsibilities include directing NIST CSF risk assessments, developing security governance and compliance programs, advising C-suite clients, supporting proposals and sales pursuits, designing cyber roadmaps and operating models, reviewing incident response plans, and managing third-party cyber risk. The role also involves mentoring junior staff, communicating complex cyber topics, and supporting innovative AI and ML-enabled cybersecurity services.

Leads the global Rewards technology roadmap across HRIS, compensation planning, analytics, automation, data governance, pay equity, and AI initiatives. The role configures and improves Rewards systems, builds dashboards and self-service tools, partners with HR Technology and IT on integrations, and supports EU pay transparency compliance. It also translates business needs into scalable technology solutions and insights for senior decision-makers.

Build and deploy full-stack data science and AI solutions for HR, including AI agents and natural-language reporting tools. Architect Azure-based integrations with Workday and Power Apps, develop data pipelines, REST APIs, backend services, and production machine learning workflows. Apply software engineering and MLOps practices for deployment, monitoring, governance, testing, containerization, and CI/CD. Collaborate with global teams and business leaders to deliver scalable workforce analytics solutions.

Develop graduate-level Applied AI course materials, including lessons, labs, case studies, assignments, rubrics, projects, readings, templates, and glossaries. Design practical activities using LLMs, RAG, AI agents, APIs, automation, and evaluation tools. Collaborate with learning designers and university faculty, explain complex concepts to nontechnical learners, revise content based on academic review, and keep materials current with evolving AI tools and regulations.

Own the roadmap and outcomes for data products supporting credit, BNPL, fraud, underwriting, acquiring, and payments decisions. Translate business, risk, and ML needs into buildable specifications, align Data Engineering and ML teams, lead AI-first delivery, manage up to three Data Consultants, and track adoption, usage, and data-service metrics. The role requires strong fintech domain knowledge, hands-on SQL, feature-store and decisioning-data expertise, and the ability to influence cross-functional stakeholders.

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.