{"id":3454213,"url":"https://alion.io/job/evolven-data-scientist","title":"Data Scientist","company":{"id":2818066,"name":"Evolven","domain":"evolven.com","url":"https://alion.io/company/evolven","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["Ukraine"],"countries":["UA"],"hiring_countries":["UA"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":23000,"max_usd":61000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2679},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"GCP","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Python","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"A/B Testing","optional":true},{"name":"Computer Vision","optional":true},{"name":"Databricks","optional":true},{"name":"MLFlow","optional":true},{"name":"NLP","optional":true},{"name":"PyTorch","optional":true},{"name":"Recommender Systems","optional":true},{"name":"Snowflake","optional":true},{"name":"Spark","optional":true},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2026-10-23T10:52:43Z","employer_posted_date":null,"last_verified_at":"2026-10-23T10:52:43Z","board_verified":false,"closed_at":null,"days_open":10,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":10},"description":"We’re looking for a Senior Data Scientist who can take a data problem from an initial business question to a solution that works in production.\n\nThis role is not only about building models. We’re looking for someone who is comfortable exploring imperfect data, challenging assumptions, choosing the right approach for the problem, and working closely with engineers and product teams to turn experiments into reliable solutions.\n\nWhat you’ll work on:\n\nExplore and analyze complex datasets to understand patterns, dependencies, and opportunities.\n\nTurn business and product questions into well-defined data science problems.\n\nDesign, train, evaluate, and improve machine learning and statistical models.\n\nWork on the full ML lifecycle: data exploration, feature engineering, experimentation, validation, deployment, and monitoring.\n\nDecide when an ML model is actually needed — and when a simpler statistical or analytical approach will do the job better.\n\nDefine meaningful metrics and design experiments to measure the impact of proposed solutions.\n\nWork with Data and ML Engineers to bring models into production and keep them reliable over time.\n\nInvestigate model performance, data quality issues, drift, and unexpected production behavior.\n\nPresent findings and recommendations in a way that is useful to both technical teams and business stakeholders.\n\nHelp shape our data science practices through code reviews, technical discussions, and mentoring.\n\nWhat we’re looking for:\n\n5+ years of hands-on experience in Data Science or Machine Learning.\n\nStrong Python skills and practical experience with Pandas, NumPy, scikit-learn, or similar tools.\n\nStrong foundation in statistics, probability, and machine learning.\n\nExperience working with real-world datasets where the data is not always clean, complete, or perfectly structured.\n\nConfident SQL skills.\n\nExperience building ML solutions beyond notebooks and prototypes.\n\nGood understanding of model validation, experimentation, feature engineering, and performance evaluation.\n\nExperience with production ML workflows and familiarity with MLOps practices.\n\nExperience with AWS, GCP, or 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