{"id":2768967,"url":"https://alion.io/job/xynvia-data-science-intern","title":"Data Science Intern","company":{"id":4442797,"name":"xynvia","domain":"xynvia.com","url":"https://alion.io/company/xynvia","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Early Careers","role_family":"Early Careers","seniority":"intern","employment_type":"internship","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":7500,"max_usd":15000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":829},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Computer Vision","optional":false},{"name":"Docker","optional":false},{"name":"ETL/ELT","optional":false},{"name":"FastAPI","optional":false},{"name":"Flask","optional":false},{"name":"Git","optional":false},{"name":"GitHub","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Matplotlib","optional":false},{"name":"NLP","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Plotly","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Seaborn","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false}],"status":"live","first_seen_at":"2026-10-14T04:12:07Z","employer_posted_date":null,"last_verified_at":"2026-10-14T04:12:07Z","board_verified":false,"closed_at":null,"days_open":16,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":16},"description":"All careers Open role / Internship\n InternshipIntermediate foundation Kozhikode · Hybrid\n Data Science InternExtract actionable intelligence from complex datasets, prototype machine learning models, and build reproducible data pipelines supporting Xynvia's healthcare and enterprise AI systems.\nApply for this role 01Intermediate capability 02Real product work 03Hybrid collaboration Role standard\nReady to contribute. Still ready to learn.\nAt least one end-to-end data science project or a credible portfolio demonstrating exploratory data analysis (EDA), statistical modelling, feature engineering, and model evaluation.\nWhat matters most\nShow us how you think, what you have built or learned, and how you improve work after feedback.\nResponsibilities\nWhat you will own.\n01Clean, explore, and preprocess structured and unstructured data to extract meaningful patterns and baseline metrics.\n02Develop, evaluate, and benchmark machine learning and statistical models using sound validation techniques.\n03Build and maintain reproducible data pipelines, ETL scripts, and analytical workflows for real-world datasets.\n04Collaborate with engineering teams to package and serve model prototypes via lightweight APIs (FastAPI / Flask).\n05Document analytical methodologies, dataset assumptions, validation results, and experiment findings with clarity.\nIntermediate skills\nWhat you should bring.\nProficiency in Python and core data libraries (Pandas, NumPy, Scikit-Learn)\nStrong foundation in statistics, hypothesis testing, probability, and exploratory data analysis\nIntermediate SQL for querying, aggregating, and structuring relational data\nWorking knowledge of machine learning algorithms (regression, classification, clustering, ensemble methods)\nFamiliarity with data visualization tools (Matplotlib, Seaborn, Plotly) and version control (Git)\nUseful, not required: Experience with PyTorch, TensorFlow, NLP / LLM concepts, computer vision basics, vector databases, FastAPI, Docker, or PostgreSQL is useful.\nEarly outcomes\nWhat good progress looks like.\n 01A validated, reproducible data pipeline or predictive model benchmarked against defined performance metrics\n 02A lightweight inference API or analytical dashboard presenting model predictions and business insights\n 03Clear experiment documentation, error analysis, and actionable recommendations for the production team\nHiring path\nClear and role-relevant.\n01\nApplication review\nResume, portfolio and evidence of practical ability.\n02\nWorking conversation\nYour thinking, interests and relevant experience.\n03\nPractical review\nA focused task or work sample when useful for the role.\n04\nFinal alignment\nScope, expectations, location and next-step clarity.\nThe sequence may vary by role. 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