Lead Quantitative Analytics Specialist Job at Wells Fargo (Bengaluru)

Lead Quantitative Analytics Specialist

Wells Fargo

Location:
India , Bengaluru

Category:
IT - Software Development

Contract Type:
Not provided

Salary:

Not provided

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Job Description:

Wells Fargo is seeking a Lead Quantitative Analytics Specialist. This is a partner-facing role and is responsible for delivering high impact analytic and data science projects across enterprise functions. AIM is the centralized model development organization for the COO and enterprise functions, delivering Predictive AI, NLP, and Generative AI solutions aligned to strategic business priorities. The group operates as a designated Model Development Center, applying robust governance, reusable components, and mature delivery practices to responsibly commercialize AI at scale.

Job Responsibility:

Lead end‑to‑end development of predictive and statistical models, including problem formulation, feature engineering, model training, evaluation, deployment, and post‑production monitoring
Apply supervised, unsupervised, semi‑supervised, and time‑series modeling techniques to solve complex business problems across COO-CAO and enterprise portfolios
Design and implement models using Python‑based ML ecosystems (e.g., scikit‑learn, XGBoost, LightGBM, PySpark ML) with strong emphasis on scalability and reproducibility
Develop and maintain production‑ready codebases following enterprise standards for version control, testing, and documentation
Own model documentation artifacts aligned with enterprise Model Risk Management (MRM) standards, including methodology, assumptions, limitations, performance metrics, and monitoring plans
Partner with Model Risk, Audit, and Compliance teams to support independent validation, issue remediation, and regulatory exams
Perform rigorous model performance analysis, stability testing, bias assessment, and error diagnostics using appropriate statistical and ML metrics (e.g., AUC‑ROC, KS, precision‑recall, stability indices)
Translate business problems into analytical problem statements and communicate complex modeling outcomes clearly to non‑technical stakeholders
Act as a trusted analytics advisor to product, operations, risk, and control partners, enabling data‑driven decision making
Present analytical findings, model insights, and recommendations to senior leadership with clarity and impact
Lead multiple analytics initiatives concurrently, ensuring on‑time, high‑quality delivery across the model lifecycle
Collaborate closely with data engineers, platform teams, BI/UI specialists, and MLOps partners to deploy and scale models
Contribute to reusable model components, accelerators, and best practices that improve AIM delivery velocity and quality
Mentor junior data scientists and analysts
contribute to building a strong Predictive AI talent pipeline

Requirements:

8+ years of Quantitative Analytics experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
Master's degree or higher in a quantitative field such as mathematics, statistics, engineering, physics, economics, or computer science
Demonstrated experience delivering multiple end‑to‑end modeling projects in production environments
Strong expertise in Python programming and core data science libraries for modeling, analysis, visualization, and automation
Deep understanding of machine learning algorithms, statistical modeling, and time‑series analysis
Proven ability to manage complex analytical problems and drive alignment across geographically distributed teams
Excellent analytical rigor, organizational skills, and attention to detail across data analysis, code management, and documentation
Demonstrated excellence at identifying stakeholders, understanding needs, and driving to resolution

Nice to have:

Excellent verbal, written, and interpersonal communication skills
Experience in Banking, Financial Services, or regulated enterprise environments
Proficiency with SQL, large‑scale data platforms (e.g., BigQuery, Hive, Spark), and distributed computing
Experience developing models using PySpark, and managing code with Git/GitHub
Familiarity with automated ML and workflow orchestration tools (e.g., H2O, DataRobot, Airflow)
Hands‑on experience with cloud platforms (GCP, Azure, AWS) for model development and deployment
Knowledge of deep learning techniques (ANN, CNN, RNN, DNN) and practical considerations for architecture design
Exposure to unstructured data problems, including NLP, text mining, or voice/digital analytics
Strong understanding of model monitoring, drift detection, and lifecycle management
Advanced proficiency in PowerPoint and Excel for executive‑level communication

Additional Information:

Job Posted:
March 20, 2026

Expiration:
April 03, 2026

Employment Type:

Fulltime

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