{"id":3357441,"url":"https://alion.io/job/codvo-senior-data-scientist","title":"Senior Data Scientist","company":{"id":3800246,"name":"Codvo","domain":"codvo.ai","url":"https://alion.io/company/codvo","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":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"explicit","locations":["Pune, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":19500,"max_usd":42000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":75},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Anomaly Detection","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"GCP","optional":false},{"name":"LightGBM","optional":false},{"name":"LLM","optional":false},{"name":"NumPy","optional":false},{"name":"Prophet","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"Time Series","optional":false},{"name":"XGBoost","optional":false},{"name":"AI Agents","optional":true},{"name":"Amazon SageMaker","optional":true},{"name":"Apache Kafka","optional":true},{"name":"Databricks","optional":true},{"name":"Docker","optional":true},{"name":"Kubernetes","optional":true},{"name":"LangChain","optional":true},{"name":"LlamaIndex","optional":true},{"name":"MLFlow","optional":true},{"name":"Power BI","optional":true},{"name":"Spark","optional":true},{"name":"Tableau","optional":true}],"status":"live","first_seen_at":"2026-10-27T17:25:38Z","employer_posted_date":null,"last_verified_at":"2026-10-27T17:25:38Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"Job title : Sr. Data Scientist – Predictive Intelligence Engine (PIE)\nLocation: Pune, India (hybrid) \nExperience : 4-9 years\nType: Full-time\n\nAbout Codvo.ai\nCodvo.ai is an AI-native technology services company that helps global enterprises build and scale data, cloud and AI solutions.\n\nRole overview\nWe are hiring a Data Scientist with hands-on Predictive Intelligence Engine (PIE) experience to turn enterprise and industrial data into models that forecast outcomes and prevent failures. You will own problems end to end: framing the question, building and validating models, and putting them into production with engineering teams. The role works directly with client stakeholders and suits someone who enjoys both technical depth and business impact.\n\nKey responsibilities\nDesign, build and enhance models and pipelines within the Predictive Intelligence Engine (PIE), partnering with stakeholders to translate problems into measurable predictive use cases.\nBuild, validate and tune predictive models for forecasting, failure prediction, risk scoring, churn and demand.\nDevelop predictive maintenance and anomaly detection solutions using sensor, IoT and time-series data from industrial assets.\nEngineer features from large structured and unstructured datasets, including historian and operational data.\nApply statistical methods, survival analysis and experiment design to measure model impact.\nBuild and evaluate GenAI / LLM solutions such as RAG pipelines where they strengthen predictive workflows.\nWork with data and ML engineers to deploy models into production and monitor accuracy, drift and retraining (MLOps).\nPresent predictions and their business value to technical and non-technical audiences through dashboards and clear recommendations.\nDocument models, assumptions and results, following data governance and compliance standards.\nMentor junior team members and contribute to reusable predictive assets and best practices.\n\nRequired qualifications\nBachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, Engineering or a related field.\n4–7 years of hands-on data science experience, with at least 3 years building predictive models.\nHands-on experience building or working on a Predictive Intelligence Engine (PIE) in production, such as predictive maintenance, demand forecasting or failure / risk prediction.\nStrong time-series forecasting skills (ARIMA, Prophet, LSTM or gradient-boosted models such as XGBoost / LightGBM).\nStrong Python skills, including pandas, NumPy, scikit-learn and TensorFlow or PyTorch.\nAdvanced SQL and experience working with large datasets.\nSolid grounding in statistics, probability, model validation and performance metrics.\nWorking knowledge of a cloud platform: Azure, AWS or GCP.\nClear communication skills and comfort working with client stakeholders.\n\nPreferred qualifications :\nExperience with industrial data sources such as AVEVA PI / OSIsoft PI historians, SCADA, IoT and sensor streams.\nExperience with real-time or streaming prediction (Kafka, Spark Streaming, Azure Event Hubs).\nMLOps tools such as MLflow, Docker, Kubernetes, Azure ML, SageMaker or Databricks.\nExperience with GenAI and LLMs: RAG, vector databases, LangChain / LlamaIndex or agentic AI workflows.\nVisualization tools such as Power BI or Tableau.\nDomain exposure to Oil & Gas, Energy, LNG, Manufacturing or Healthcare / MedTech.\nExperience in a client-facing consulting or services 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