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 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.

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.

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.

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.

Lead clinical data science and statistical analyses for pharmaceutical development programs. Develop analysis-ready datasets, reproducible workflows, visualizations, simulations, and quantitative insights using clinical trial, biomarker, real-world, and external data. Support integrated data review, data quality assessment, regulatory compliance, and evidence-based study decisions. Collaborate with cross-functional teams, review internal and external deliverables, advance automation and AI/ML adoption, and mentor less experienced colleagues.

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.

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.

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.

Leads a data science team developing, deploying, monitoring, and improving advanced analytics models and decision-support solutions. Translates clinical, research, and operational needs into prioritized analytics work; supervises, coaches, and develops data scientists; and partners with technical and business stakeholders on architecture, data pipelines, governance, compliance, and production deployment. Supports analytics strategy, roadmaps, reusable data products, and scalable solutions across a regulated healthcare environment.

Designs and deploys scalable AI solutions for enterprise customers. Responsibilities include gathering technical requirements, building integrations and prototypes, leading architecture workshops, troubleshooting production issues, documenting reusable practices, and collaborating with Product and Engineering teams to improve the platform.

Develop and deploy machine learning models for retrieval, reranking, indexing, search relevance, crawling, and content understanding. Build large-scale embedding and retrieval systems, evaluation pipelines, and quality metrics for agent-native search. Integrate models into high-throughput production services while balancing latency, quality, and cost. Collaborate on architecture and product decisions involving search, retrieval, and LLM-based systems.

Own and operate infrastructure security engineering across detection engineering, incident response, AWS and Kubernetes security, endpoint hardening, DLP, vulnerability management, and security automation. Build SIEM detections, ML-based analytics, and LLM-powered workflows while investigating incidents and driving remediation. Secure Linux, macOS, and Windows environments, perform network analysis and penetration testing, and partner with engineering teams to improve security across production infrastructure.

Design, prototype, and optimize software systems for machine-learning and agentic applications. Build training and inference pipelines across edge devices, GPU servers, HPC clusters, and cloud environments. Evaluate ML frameworks, serving technologies, accelerators, and development tools; collaborate with researchers, engineers, universities, vendors, and business units. Disseminate results through publications, patents, and technical reports while providing technical consultation.

Design, develop, and deploy agentic AI systems and intelligent automation solutions on Azure. Build LLM, RAG, and machine learning applications; create data workflows with Databricks and PySpark; implement CI/CD, monitoring, security, and observability; optimize production performance and costs; and collaborate with engineering, product, and business teams.

Build production-grade software, data pipelines, analytical applications, and AI solutions for vegetable R&D. Integrate scientific data, models, workflows, and domain logic into scalable cloud-native tools. Deploy and monitor machine learning models, manage production environments, collaborate with scientists and data teams, and evaluate agentic AI capabilities. Ensure solutions are explainable, reliable, secure, compliant, and aligned with responsible AI and governance practices.

Own the end-to-end strategy, roadmap, and outcomes for a network intelligence and security product serving ISP customers. Partner with engineering, data science, security, and compliance teams on architecture, ML evaluation, privacy, and production delivery. Conduct customer discovery, analyze market and threat trends, define requirements and metrics, build business cases, and represent the product to technical customers, executives, sales, marketing, and public audiences.

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 delivery with Data Engineering and MLOps, and lead AI-first product practices. Manage up to three Data Consultants, capacity planning, stakeholder alignment, adoption metrics, data SLAs, and self-serve analytics across multiple product lines.

Lead Salesforce product initiatives supporting global sales teams. Translate seller and sales operations needs into requirements, roadmaps, business processes, and Salesforce solutions. Gather cross-regional feedback, oversee implementation and rollout, improve adoption, support reporting, and coordinate training and communications. Partner with sales, IT, architects, and technical teams while evaluating AI, machine learning, and new Salesforce capabilities to reduce administrative burden and improve business impact.

Leads the data and AI function for a new fintech platform, owning strategy, organization, budget, data engineering, ML/MLOps, data products, feature stores, agentic systems, governance, and real-time credit and risk decisioning. Builds the function from scratch, establishes production AI standards, oversees platform reliability and observability, partners on architecture and risk, and reports to executive and board-level stakeholders.

Owns and evolves Moderna’s global SAP S/4HANA Accounts Receivable and cash application ecosystem. The role configures AR, credit management, lockbox, electronic bank statements, clearing, banking integrations, e-invoicing, and payment platforms. It translates business requirements into functional specifications, leads testing and rollouts, ensures SOX compliance, and drives automation using AI, machine learning, and RPA. The position also provides global training, documentation, and support across Finance, Treasury, and Digital teams.

Lead enterprise AI strategy and advisory engagements, defining roadmaps, prioritizing use cases, building AI Centers of Excellence, and guiding initiatives into production. Advise C-level stakeholders, assess emerging technologies, develop investment cases, drive AI adoption and change management, facilitate strategic workshops, and support proposals and client relationships. The role requires strong AI expertise, strategic communication, stakeholder management, and technical judgment without daily hands-on implementation.