{"id":2776624,"url":"https://alion.io/job/quest-global-data-scientist","title":"Data Scientist","company":{"id":59490,"name":"Quest Global","domain":"questglobal.com","url":"https://alion.io/company/quest-global","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":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Chennai, 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":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Pandas","optional":false},{"name":"Python","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Time Series","optional":false},{"name":"SHAP","optional":true}],"status":"live","first_seen_at":"2026-09-27T17:32:14Z","employer_posted_date":null,"last_verified_at":"2026-09-27T17:32:14Z","board_verified":false,"closed_at":null,"days_open":32,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":32},"description":"Job Requirements\n\nAbout the Role\n\nWe are seeking a Data Scientist with 5+ years of experience to develop machine learning solutions for failure prediction, classification, and fault analysis in semiconductor manufacturing and equipment systems. This role focuses on time-series modeling, equipment health monitoring, and root-cause analysis using structured reliability methods such as fault tree analysis (FTA).\n\nYou will work with complex, high-volume data from semiconductor tools (sensor signals, logs, process data) to improve tool uptime, yield, and operational reliability.\n\nKey Responsibilities\n\nDesign, develop, and deploy machine learning models for equipment failure prediction and fault classification\nAnalyze time-series data from semiconductor tools (sensor telemetry, logs, process traces)\nPerform advanced feature engineering (lags, rolling windows, trends, seasonality, event-based features)\nApply fault tree analysis (FTA) concepts to support root-cause analysis and improve model interpretability\nCollaborate with process engineers, equipment engineers, and failure analysis teams\nSelect, justify, and evaluate appropriate ML algorithms\nValidate models using metrics such as precision/recall, F1-score, ROC-AUC, and early failure detection accuracy\nDocument models, assumptions, and results for technical and cross-functional stakeholders\nMentor junior data scientists and contribute to best practices\n\nWork Experience\n\nRequired Qualifications\n\n5+ years of professional experience as a Data Scientist or Machine Learning Engineer\nStrong proficiency in Python (Pandas, NumPy, scikit-learn)\nProven experience with time-series data modeling\nHands-on experience building classification and predictive models\nExperience with failure prediction, reliability analytics, or equipment health monitoring\nWorking knowledge of fault tree analysis (FTA) or structured root-cause analysis\nStrong feature engineering skills for noisy, real-world industrial data\nAbility to clearly communicate technical results to engineering stakeholders\n\nPreferred Qualifications\n\nExperience in semiconductor manufacturing or equipment systems (etch, deposition, lithography, inspection, metrology)\nFamiliarity with process data, tool logs, alarms, and sensor telemetry\nExperience with survival analysis, RUL estimation, or anomaly detection\nExposure to model explainability techniques (e.g., SHAP, feature importance)\nExperience deploying models into production or factory systems\nBackground in reliability engineering, systems engineering, or failure analysis\n\nWhat Success Looks Like\n\nAccurate and reliable failure prediction models with low false-positive rates\nClear linkage between data-driven predictions and physical failure mechanisms\nMeasurable improvements in tool uptime, yield, and maintenance planning\nStrong collaboration with cross-functional engineering teams\n\nRepresentative Tech Stack\n\nPython (Pandas, NumPy, scikit-learn)\nTime-series analysis libraries\nMachine learning frameworks\nVisualization and reporting 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