{"id":1578745,"url":"https://alion.io/job/exl-data-scientist","title":"Data Scientist","company":{"id":38016,"name":"EXL","domain":"exlservice.com","url":"https://alion.io/company/exl","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Gurgaon, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":15500,"max_usd":38000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":13},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"LightGBM","optional":false},{"name":"LIME","optional":false},{"name":"Machine Learning","optional":false},{"name":"NumPy","optional":false},{"name":"Python","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SHAP","optional":false},{"name":"SQL","optional":false},{"name":"XGBoost","optional":false}],"status":"live","first_seen_at":"2026-10-01T11:53:06Z","employer_posted_date":null,"last_verified_at":"2026-10-01T11:53:06Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"About the job:\n\nPosition Overview:\n\nWe are seeking a skilled Modeler to contribute to the development and optimization of marketing analytics models.\n\nYou will work within a cross-functional team to build propensity models, next-best-action (NBA) engines, customer segmentation frameworks, and campaign response models that power data-driven marketing strategies.\n\nYou will apply best practices in model explainability, fairness testing, and lifecycle governance to ensure high-quality, compliant model outputs.\n\nKey Responsibilities:\n\n- Develop and optimize propensity models, segmentation frameworks, and campaign response models using supervised and unsupervised machine learning techniques.\n\n- Conduct bias testing and implement model explainability methods (e.g., SHAP, LIME) to support model approval and governance workflows.\n\n- Perform exploratory data analysis and feature engineering to identify meaningful predictors of customer behavior.\n\n- Support the full model lifecycle including development, documentation, validation support, performance monitoring, and periodic re-validation.\n\n- Collaborate with marketing and data teams to understand business objectives and translate them into modeling requirements.\n\n- Prepare clear, concise model documentation including methodology overviews, performance summaries, and limitation disclosures.\n\n- Monitor deployed models for data drift, performance degradation, and champion-challenger evaluation.\n\n- Contribute to A/B test design and campaign measurement analyses to evaluate marketing effectiveness.\n\n- Stay current on advances in marketing data science, uplift modeling, and customer analytics.\n\nQualifications & Experience:\n\n- 3 - 6 years of experience in data science, analytics, or quantitative modeling with a focus on customer or marketing analytics.\n\n- Hands-on experience building and evaluating classification and regression models for propensity scoring or segmentation.\n\n- Proficiency in Python with working knowledge of libraries such as scikit-learn, XGBoost, LightGBM, pandas, and numpy.\n\n- Familiarity with model explainability tools (SHAP, LIME) and basic concepts of model fairness and bias testing.\n\n- Experience with SQL and large-scale data platforms for data extraction and feature preparation.\n\n- Understanding of model validation principles and documentation standards.\n\n- Strong analytical and problem-solving skills with attention to detail.\n\n- Effective written and verbal communication skills for presenting model results to both technical and business stakeholders.\n\n- Bachelor's degree (Master's preferred) in Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field.\n\nModel Lifecycle & Governance:\n\nThis role supports end-to-end model lifecycle management.\n\nResponsibilities encompass model development, independent validation and assessment, performance optimization, monitoring, documentation, and governance - ensuring all models adhere to applicable standards and remain fit-for-purpose throughout their operational life.\nSkills\nData Science, Data Scientist, Analytics, Data Analytics, Data Modeling, Python, Machine Learning, SQL","description_format":"text","description_chars":3155,"description_truncated":false,"requirements":{"experience_years_min":4,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-10-01T12:00:00Z"}],"liveness":{"score":86,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.86,"p_room":1,"age_days":0,"expected_fill_days":18,"reasons":["seen:0","win:early","comp:brand"],"computed_at":"2026-10-02T01:04:54Z"},"pay":null,"html_url":"https://alion.io/job/exl-data-scientist","json_url":"https://alion.io/job/exl-data-scientist.json","meta":{"generated_at":"2026-10-02T01:04:54Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","usage":{"tier":"crawler","counted_by":"address","units_charged":1,"used_today":1130,"day_limit":5000,"remaining_today":3870,"minute_limit":60,"resets_at":"2026-10-03T00:00:00Z"}}}