{"id":963885,"url":"https://alion.io/job/link-group-senior-data-scientist-3","title":"Senior Data Scientist","company":{"id":880,"name":"Link Group","domain":"linkgroup.co","url":"https://alion.io/company/link-group","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"staffing","is_intermediary":false,"listed_via":null,"ats_vendor":"Recruitify","truth_index":{"grade":"B","score":82,"open_postings":92,"ghost_share":0,"stale_share":0.739,"repost_share":0,"time_to_fill_p50_days":44,"computed_at":"2026-11-08T05:47:12Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Warsaw, Poland"],"countries":["PL"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":66000,"max_usd":108000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":111},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"bandit","optional":false},{"name":"Databricks","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Python","optional":false},{"name":"Recommender Systems","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Spark","optional":false},{"name":"Embeddings","optional":true}],"status":"live","first_seen_at":"2026-09-09T00:00:00Z","employer_posted_date":"2026-09-16","last_verified_at":"2026-11-08T06:17:41Z","board_verified":true,"closed_at":null,"days_open":60,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":60},"description":"Senior Data Scientist\nWe are looking for a Senior Data Scientist to join a global technology organisation and work on machine learning solutions that directly influence customer engagement, personalisation and conversion.\nYou will join a cross-functional, international team responsible for building intelligent decision-making systems across customer communication and digital journeys. This is a hands-on role combining machine learning, reinforcement learning, experimentation and causal inference, with a strong focus on taking models into production and measuring their real-world impact.\nYou will work closely with Data Scientists, Software Engineers, Marketing Analytics and Product teams to develop scalable solutions that determine the best action, message or customer journey for each individual user.\nAbout the role\nYour work will focus on three key areas:\nIntelligent decisioning - designing and scaling machine learning and reinforcement learning solutions, including contextual bandits, to optimise communication channels such as email, SMS and push notifications.\nPersonalised customer journeys - developing models that use customer behaviour, context and preferences to determine the most relevant next step in the customer funnel.\nAI-assisted content & search - building intelligent systems to index, search and retrieve marketing assets such as images and copy, as well as supporting the automated creation of high-performing communication content.\nYou will have a direct impact on how organisations understand customer behaviour and make data-driven decisions at scale.\nWhat you’ll do\nBuild, own and continuously improve ML and reinforcement learning models running in production.\nDevelop decisioning logic that determines the best next action for users across customer journeys and communication lifecycles.\nDesign and evolve contextual bandit systems, including approaches such as Thompson Sampling, offline policy evaluation and low-latency deterministic inference.\nWork closely with Engineering teams on system architecture and the integration of models into production platforms.\nDefine how models make decisions, how those decisions are served at scale and how failures can be handled and rolled back safely.\nLead experimentation for decisioning models, defining success metrics, guardrails and appropriate experimental methodologies.\nApply rigorous statistical methods, including sequential testing, propensity methods and causal inference, to evaluate model performance.\nMonitor models in production, including performance, input-data quality, drift and latency.\nAnalyse customer behaviour and translate model outputs into decisions that business and marketing stakeholders can understand and trust.\nCollaborate with Engineering, Marketing Analytics, CRM and Product teams to turn data science solutions into measurable business outcomes.\nWhat you’ll bring\n5+ years of professional experience in Data Science, Machine Learning or a related quantitative field.\nA proven track record of taking machine learning models from experimentation into production.\nStrong Python skills and experience working with modern ML/data platforms such as Databricks, Spark and MLflow.\nStrong knowledge of machine learning for personalisation and recommendation systems.\nHands-on experience with reinforcement learning and/or contextual bandits, such as Thompson Sampling, epsilon-greedy approaches and reward design.\nStrong understanding of experimentation and causal inference, including designing and analysing controlled experiments in high-traffic environments.\nExperience with classical machine learning techniques such as regression, classification, clustering and recommendation systems.\nExperience working with customer behaviour data, segmentation and production feature pipelines.\nStrong statistical reasoning and the ability to evaluate model performance and experimental results accurately.\nExcellent communication skills and the ability to work effectively with technical and non-technical stakeholders.\nA pragmatic, curious and collaborative approach to problem-solving.\nNice to have\nExperience with off-policy evaluation, counterfactual learning or propensity methods.\nKnowledge of sequential testing, including SPRT.\nExperience with experimentation platforms such as Statsig or similar tools.\nExperience with streaming systems and low-latency model inference.\nExperience using unstructured data as model features, including embeddings and text signals.\nBackground in marketing, CRM decisioning, customer communications or funnel/conversion optimisation.\nWhat you can expect\nWork on large-scale machine learning systems with millions of users.\nDirect impact on personalisation, customer experience and business performance.\nCollaboration with experienced Data Scientists, Engineers, Product Managers and Analytics professionals across international teams.\nA modern technology stack and opportunities to influence ML architecture and experimentation practices.\nEnd-to-end ownership of projects, from problem definition and experimentation through to production and continuous optimisation.\nExposure to advanced areas of ML, including reinforcement learning, contextual bandits, causal inference and AI-powered decisioning.\nA highly collaborative, autonomous and product-oriented working environment.\nAccess to a modern office in Warsaw, with flexible employee facilities.\nWe are looking for someone who enjoys solving complex problems with data, is comfortable working with ambiguity and can combine strong technical expertise with a clear understanding of business and customer needs.\nEven if you do not meet every requirement but have strong experience in production Data Science and believe you could thrive in this environment, we would be interested in hearing from you.","description_format":"text","description_chars":5797,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["IT Outsourcing & Dedicated Teams","Managed IT Services (MSP)","IT Staffing & Staff 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