{"id":150021,"url":"https://alion.io/job/lingaro-data-scientist","title":"Data Scientist","company":{"id":53383,"name":"Lingaro","domain":"lingarogroup.com","url":"https://alion.io/company/lingaro","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"A","score":86,"open_postings":26,"ghost_share":0,"stale_share":0.577,"repost_share":0,"time_to_fill_p50_days":42,"computed_at":"2026-10-23T05:54:15Z"}},"role":"Data Science","role_family":"Data Science","seniority":null,"employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Poland"],"countries":["PL"],"hiring_countries":["PL"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":57000,"max_usd":101000,"period":"year","method":"role_country_seniority_unknown","sample_n":367},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure","optional":false},{"name":"Databricks","optional":false},{"name":"GCP","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Time Series","optional":false},{"name":"Reinforcement Learning","optional":true},{"name":"Scala","optional":true}],"status":"live","first_seen_at":"2025-11-07T13:32:25Z","employer_posted_date":"2025-11-07","last_verified_at":"2026-10-23T10:20:17Z","board_verified":true,"closed_at":null,"days_open":350,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":349},"description":"Our team is delivering business solutions with Machine Learning and Data Science often turning them into scalable platforms including non-trivial and innovative solutions in the space of Forecasting and Customer Analytics often leveraging cutting-edge Causality frameworks. Example projects in this area: Next Best Offer/Action, propensity modeling, churn modeling, forecasting of demand and sales, revenue growth management, etc. This role requires close collaboration with business stakeholders, and the ability to understand their problems and translate them into machine learning ones.\nTasks:\nWork on end-to-end classification and forecasting use cases: problem framing, data preparation, model development, evaluation and basic deployment support (e.g. demand forecasting, churn prediction).\nExplore and clean data; perform EDA to understand data and flag data quality issues.\nEngineer features for tabular and time-series data.\nTrain, validate, and tune standard ML models (e.g. logistic regression, tree-based models, gradient boosting, simple neural nets, classical time-series models).\nEvaluate models with appropriate metrics that have impact on business KPIs.\nBuild clear visualizations and concise reports to present model results and insights to business stakeholders.\nCollaborate with data engineers and AI engineers to bring models into production (batch scoring, APIs, models monitoring, dashboards).\nDocument data sources, modeling assumptions, and experiment results in a reproducible way (notebooks, reports, wikis).\nBusiness understanding and translating problems into technical goals by defining success metrics, auditing data feasibility, and aligning stakeholder expectations.\nPre-sales activities (at senior consultant level).\nRequirements:\nCommercial experience with various classical data science and Machine Learning (ML) models (e.g. decision trees, ensemble-based tree models, linear regression etc.).\n\nSolid knowledge of customer analytics concepts or advanced forecasting.\n\nModel hyperparameter tuning.\n\nModel validation frameworks.\n\nExperience with business requirements gathering, transforming them into technical plan, data processing, feature engineering, models evaluation.\n\nPrevious experience in an analytical role supporting business will be a plus.\n\nFluency in Python, basic working knowledge of SQL.\n\nKnowledge of specific DS/ML libraries.\n\nSolid experience in one of the cloud computing platforms (Databricks or GCP or Azure).\n\nWhat Will Set You Apart:\nUnderstanding of Causal machine learning.\nExperience in working with big data and distributed environments would be a plus.\nCommercial experience proven by multiple successful projects in the areas of forecasting would be a big plus.\nExperience with OOP in Python.\nExperience with MLOps.\nFamiliarity with other languages R, Scala would be a plus.\nGeneral:\nBasic computer programming skills and familiarity with programming concepts.\nStrong business acumen.\nExperience with deep learning, reinforcement learning or other advanced modeling concepts in Classical Data Science problems.\nAbility to come up with creative solutions to address customer 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