{"id":1285204,"url":"https://alion.io/job/morgan-stanley-data-scientist-director-data-analytics-engineering","title":"Data Scientist - Director - Data & Analytics Engineering","company":{"id":40230,"name":"Morgan Stanley","domain":"morganstanley.com","url":"https://alion.io/company/morgan-stanley","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Eightfold","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"head","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":36000,"max_usd":86000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":45},"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Hadoop","optional":false},{"name":"Machine Learning","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":true}],"status":"live","first_seen_at":"2026-09-11T00:00:00Z","employer_posted_date":"2026-09-21","last_verified_at":"2026-09-27T00:56:06Z","board_verified":true,"closed_at":null,"days_open":16,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":16},"description":"Morgan Stanley is seeking a Data Scientist to join the CDRR Technology team within the Fraud Department. The position is responsible for the development of statistical and machine learning models used to identify, assess, and mitigate fraud risk across Morgan Stanley products.\nThe successful candidate will independently execute model development assignments, including exploratory data analysis, analytical dataset construction, feature engineering, algorithm selection, model training, performance evaluation, and technical documentation. The role requires demonstrated expertise in the mathematical and statistical foundations of classical supervised and unsupervised machine learning methods and the ability to apply those methods to large, complex, and imperfect real-world datasets.\nThis is a Director-level individual contributor position and does not include formal people-management responsibilities. The individual will manage assigned model development projects with guidance from senior members of the team. The individual will also provide technical guidance and mentoring to junior Data Scientists and Data Engineers.\nSince 1935, Morgan Stanley is known as a global leader in financial services, always evolving and innovating to better serve our clients and our communities in more than 40 countries around the world.\nWhat you'll do in the role:\nIndependently execute end-to-end model development, including technical documentation.\nDevelop and evaluate models for highly imbalanced, non-stationary, and adversarial environments where fraud patterns, customer behaviour, and operational processes evolve over time.\nApply statistical and mathematical principles to model selection, validation, and performance assessment, including the treatment of class imbalance, overfitting, data leakage, missing data, and model stability.\nMonitor deployed models, diagnose performance degradation, assess emerging fraud patterns, and recommend recalibration, redevelopment, threshold changes, or retirement as appropriate.\nWhat you'll bring to the role:\n6+ years of professional experience in data science, machine learning, statistical modeling, or quantitative analytics.\nDemonstrated depth of knowledge in statistical inference, probability, sampling, hypothesis testing, regularization, bias-variance trade-offs, optimization, feature selection, dimensionality reduction, model calibration, and statistical diagnostics.\nHands-on experience performing exploratory data analysis, constructing analytical datasets, and engineering features from large, complex, and imperfect real-world data.\nAdvanced proficiency in Python and SQL, and experience with Hadoop, Hive, Impala, Spark, or PySpark.\nExperience designing statistically valid training, validation, and testing approaches and evaluating models using appropriate performance, calibration, stability, and diagnostic measures.\nDemonstrated ability to independently manage model development assignments and produce technical documentation suitable for review and governance.\nExcellent written and verbal communication skills. Including the ability to communicate complex analytical methods and results to technical and non-technical stakeholders.\nGood to have:\nExperience developing machine learning or statistical models for fraud detection, financial crime, transaction monitoring, payment risk, or anomalous-behavior detection.\nExperience analyzing high-volume transactional, account, client, or behavioral data within financial services.\nKnowledge of fraud typologies, risk indicators, and security issues applicable to banking or Wealth Management.\nExperience developing models within a regulated environment subject to Model Risk Management, validation, documentation, and governance requirements.\nExperience with data-visualization and reporting tools such as Tableau.lmk\nWHAT YOU CAN EXPECT FROM MORGAN STANLEY:\nAt Morgan Stanley, we raise, manage and allocate capital for our clients - helping them reach their goals. We do it in a way that’s differentiated - and we’ve done that for 90 years. Our values - putting clients first, doing the right thing, leading with exceptional ideas, committing to diversity and inclusion, and giving back - aren’t just beliefs, they guide the decisions we make every day to do what's best for our clients, communities and more than 80,000 employees in 1,200 offices across 42 countries. At Morgan Stanley, you’ll find an opportunity to work alongside the best and the brightest, in an environment where you are supported and empowered. Our teams are relentless collaborators and creative thinkers, fueled by their diverse backgrounds and experiences. We are proud to support our employees and their families at every point along their work-life journey, offering some of the most attractive and comprehensive employee benefits and perks in the industry. There’s also ample opportunity to move about the business for those who show passion and grit in their work.\nTo learn more about our offices across the globe, please copy and paste https://www.morganstanley.com/about-us/global-offices into your browser.\nMorgan Stanley is an equal opportunity employer committed to building and maintaining a workforce that is diverse in experience and background. Our recruiting efforts reflect our strong commitment to a culture of inclusion, where individuals are hired, developed, and advanced based on their skills and talents.\nOur workforce reflects a broad cross-section of the global communities in which we operate, bringing a variety of backgrounds, talents, perspectives, and experiences.\nFor more information, please visit: https://www.morganstanley.com/people-opportunities/eeo.","description_format":"text","description_chars":5680,"description_truncated":false,"requirements":{"experience_years_min":6,"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":["Wealth Management & Financial Advisors","Asset Management & Funds","Investment Banking & M&A Advisory","Online Brokerage & Trading Platforms"],"lifecycle":[{"event":"open","at":"2026-09-26T04:28:36Z"}],"liveness":{"score":76,"band":"hot","label":"Hiring now","p_open":1,"p_active":0.846,"p_room":0.9,"age_days":15,"expected_fill_days":36,"reasons":["conf:1","win:mid","comp:brand"],"computed_at":"2026-09-26T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/morgan-stanley-data-scientist-director-data-analytics-engineering","json_url":"https://alion.io/job/morgan-stanley-data-scientist-director-data-analytics-engineering.json","meta":{"generated_at":"2026-09-27T03:19:09Z","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":3121,"day_limit":5000,"remaining_today":1879,"minute_limit":60,"resets_at":"2026-09-28T00:00:00Z"}}}