{"id":1227086,"url":"https://alion.io/job/altimetrik-data-scientist-aiml","title":"Data Scientist - AI/ML","company":{"id":1985,"name":"Altimetrik","domain":"altimetrik.com","url":"https://alion.io/company/altimetrik","size_band":"51-200","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":27000,"max_usd":57000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":9},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon SageMaker","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude","optional":false},{"name":"Databricks","optional":false},{"name":"GCP","optional":false},{"name":"LightGBM","optional":false},{"name":"LLM","optional":false},{"name":"PCI DSS","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"Vertex AI","optional":false},{"name":"XGBoost","optional":false},{"name":"Apache Kafka","optional":true},{"name":"Flink","optional":true},{"name":"Machine Learning","optional":true}],"status":"live","first_seen_at":"2026-09-25T11:57:37Z","employer_posted_date":null,"last_verified_at":"2026-09-25T11:57:37Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"Position :\n\nKey Responsibilities :\n\nModel Development :\n\n- Design, train, validate, and deploy ML models for use cases such as: real-time transaction fraud detection, card-not-present (CNP) risk scoring, authorization decline/approval optimization, chargeback/dispute prediction, merchant risk scoring, and AML/transaction-monitoring anomaly detection.\n\n- Engineer features from transactional, behavioral, and device/network data while respecting strict latency budgets (often sub-100ms scoring at authorization time).\n\n- Evaluate and select appropriate techniques - gradient boosting, deep learning, graph-based fraud detection, anomaly detection, time-series methods, based on the problem, not fashion.\n\nProductionization & MLOps :\n\n- Work with Engineering to deploy models into real-time and batch pipelines, ensuring reliability, monitoring, and rollback safety in a payments-critical path.\n\n- Build and maintain model monitoring for drift, data quality, and performance degradation, with alerting tied to operational and fraud-loss KPIs.\n\n- Contribute to (or help establish) MLOps practices: versioning of models/features/data, reproducible training pipelines, CI/CD for models, and A/B or shadow-testing frameworks before full rollout.\n\nAI/LLM-Adjacent Work :\n\n- Apply modern AI tooling - including LLMs such as Claude - to accelerate data science workflows: automated feature exploration, model documentation, anomaly narrative generation for fraud analysts, and code generation/review for data pipelines.\n\n- Explore applied use cases for LLMs in risk and compliance narratives (e.g., summarizing suspicious activity patterns for SAR drafting support, with mandatory human review).\n\nCross-Functional Collaboration & Governance :\n\n- Partner with Fraud & Risk and Compliance teams to ensure model outputs are explainable and defensible to auditors, regulators, and card scheme risk teams (Visa, Mastercard).\n\n- Present findings and model performance to non-technical stakeholders (Risk Committee, Product, Client Services) in clear, decision-useful terms.\n\n- Ensure all data handling complies with PCI DSS, data residency requirements, and internal data governance policies - particularly around cardholder data (PANs, CVVs, authentication data).\n\nRequired Qualifications :\n\n- 8+ years of overall experience, including at least 3+ years as a Data Scientist or ML Engineer, ideally in payments, fintech, banking, or another environment with high-volume transactional data and real-time decisioning.\n\n- Strong proficiency in Python (pandas, scikit-learn, XGBoost/LightGBM, PyTorch or TensorFlow) and SQL.\n\n- Demonstrated experience building and deploying models into production (not just research/exploratory work), with attention to monitoring and retraining considerations.\n\n- Solid understanding of classification, anomaly detection, and imbalanced-class problems (fraud is a classic rare-event problem).\n\n- Experience with cloud data/ML infrastructure (AWS/GCP/Azure - e.g., SageMaker, Vertex AI, Databricks) and standard data engineering tools (Spark, Airflow, or similar).\n\n- Understanding of the regulatory and security constraints of financial services data (PCI DSS, data minimization, access controls).\n\n- Strong communication skills - able to translate model outputs and trade-offs (precision/recall, false positive cost, latency) into business decisions for risk and product stakeholders.\n\nPreferred Qualifications :\n\n- Direct experience with payments-specific fraud typologies: CNP fraud, account takeover, first-party fraud, synthetic identity, BIN attacks, or card testing.\n\n- Experience with graph-based or network analysis techniques for fraud rings/merchant collusion detection.\n\n- Familiarity with card scheme rules and risk parameters (Visa Risk Manager, Mastercard Fraud attributes, or similar).\n\n- Experience applying LLMs (e.g., Claude, GPT) to data science workflows - feature engineering assistance, automated EDA, report generation, or analyst-facing summarization tools.\n\n- Exposure to real-time streaming architectures (Kafka, Flink) for low-latency scoring.\n\n- MSc/PhD in a quantitative field (Statistics, Computer Science, Applied Math, Physics, Operations Research) or equivalent practical experience.\nSkills\nPython, SQL, Machine Learning, Data Science, Scikit-Learn, PyTorch, Tensorflow, AWS, Spark, Artificial Intelligence, Data Scientist","description_format":"text","description_chars":4374,"description_truncated":false,"requirements":{"experience_years_min":8,"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":["Artificial Intelligence","IT Consulting & Digital Transformation","IT Outsourcing & Dedicated Teams","Custom Software Development"],"lifecycle":[{"event":"open","at":"2026-09-25T13:06:44Z"}],"liveness":{"score":90,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.903,"p_room":1,"age_days":2,"expected_fill_days":42,"reasons":["seen:2","velocity","win:early"],"computed_at":"2026-09-28T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/altimetrik-data-scientist-aiml","json_url":"https://alion.io/job/altimetrik-data-scientist-aiml.json","meta":{"generated_at":"2026-09-28T06:05:25Z","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":4223,"day_limit":5000,"remaining_today":777,"minute_limit":60,"resets_at":"2026-09-29T00:00:00Z"}}}