{"id":3351234,"url":"https://alion.io/job/credit-saison-senior-data-scientist","title":"Senior Data Scientist","company":{"id":841,"name":"Credit Saison","domain":"creditsaison.in","url":"https://alion.io/company/credit-saison","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":null,"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":22000,"max_usd":47000,"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":"Interpretability","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":"Cassandra","optional":true},{"name":"Hadoop","optional":true},{"name":"HBase","optional":true},{"name":"Keras","optional":true},{"name":"Spark","optional":true},{"name":"TensorFlow","optional":true}],"status":"live","first_seen_at":"2026-10-27T18:35:45Z","employer_posted_date":null,"last_verified_at":"2026-10-27T18:35:45Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"We are looking for an experienced Data Scientist to drive the development and deployment of credit risk models, predictive analytics, and machine learning solutions across the lending lifecycle. The ideal candidate combines strong expertise in Python, SQL, statistics, and machine learning with hands-on experience in application, behavioral, and collections scorecards. You will own end-to-end data science initiatives, from problem formulation and feature engineering to model development, production deployment, and continuous performance monitoring.\n\nResponsibilities: \nDesign, develop, and optimize credit risk models across the lending lifecycle, including application, behavioral, and collections scorecards.\nDevelop predictive models for loan pricing, loss forecasting, and cross-sell opportunities.\nApply statistical and machine learning techniques, including logistic regression, random forest, gradient boosting, bagging, and dimensionality reduction.\nEvaluate model performance, robustness, interpretability, and business impact.\nOwn the complete machine learning lifecycle, including data exploration, preprocessing, feature engineering, model training, validation, and deployment.\nBuild scalable training pipelines and support production-grade machine learning implementations.\nContinuously improve model accuracy, stability, and effectiveness through experimentation and iteration.\nDebug data pipelines, model performance issues, and unexpected production behavior.\nCollaborate with Risk, Product, Business, and Engineering teams to identify high-impact data science opportunities.\nDefine and execute data science roadmaps aligned with business priorities.\nIdentify and integrate internal and external data sources to improve underwriting and risk decisions.\nTranslate complex analytical findings into actionable business recommendations.\nPartner with Engineering teams to deploy models into production environments.\nSupport model infrastructure planning, implementation, and quality assurance.\nDevelop monitoring frameworks and dashboards to track model performance and business outcomes.\nDrive continuous model refinement based on evolving business requirements and data patterns.\nEvaluate emerging machine learning techniques and assess their applicability to business problems.\nDrive technical discussions on modelling approaches, feature selection, and architecture.\nProvide technical guidance, review modelling approaches, and influence data science strategy across teams when operating at lead or staff level.\n\nRequirements: \n5-10 years of experience in data science, machine learning, or statistical modelling.\nAt least 1 year of relevant experience in credit risk, lending analytics, or collections analytics.\nHands-on experience developing at least one of the following: application scorecards, behavioral scorecards, collections scorecards, loan pricing models, credit loss forecasting models, or cross-sell models.\nStrong programming proficiency in Python and SQL.\nSolid foundation in statistics, probability, hypothesis testing, regression, and feature engineering.\nPractical experience with machine learning algorithms, including logistic regression, decision trees, random forest, and boosting techniques.\nHands-on experience with Pandas, NumPy, Scikit-learn, or equivalent machine learning libraries.\nExperience building, validating, and improving predictive models using real-world business datasets.\nStrong analytical thinking, problem-solving, and stakeholder communication skills.\nYou have built credit risk models that directly influence lending decisions.\nYou understand both the mathematics behind predictive modelling and its real-world business application.\nYou are comfortable owning machine learning models from experimentation through production.\nYou enjoy solving complex lending and risk problems using data.\nYou can independently drive projects and collaborate effectively with Product, Risk, and Engineering teams.\n\nNice To Have: \nExperience with distributed computing tools such as Spark, Hadoop, Hive, or MapReduce.\nKnowledge of TensorFlow, Keras, or other deep learning frameworks.\nExperience with NoSQL databases such as MongoDB, Cassandra, or HBase.\nExposure to model deployment, monitoring, and production machine learning pipelines.\nExperience using data visualization tools and building model performance dashboards.\nExperience working in high-growth fintech, lending, NBFC, or financial services organizations.\nSkills\nML, Machine Learning, Python, SQL, Scikit-learn, Sklearn, Statistics","description_format":"text","description_chars":4542,"description_truncated":false,"requirements":{"experience_years_min":5,"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":["Financial Software & Embedded Finance","Consumer Loans & Pawnshops"],"lifecycle":[{"event":"open","at":"2026-10-27T18:35:45Z"}],"visa":[],"liveness":{"score":90,"band":"hot","label":"Hiring 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