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. The AI Innovation & Modeling (AIM) organization is building scalable, production-grade AI capabilities that power advanced Predictive AI and Generative AI solutions. We are looking for a senior, hands-on engineer who can own end-to-end data ingestion and model-ready data pipelines (batch + streaming), accelerate cloud adoption on GCP, and help establish GenAI engineering and evaluation (LLMOps) practices to improve quality, reliability, and cost in production. In this role, you’ll partner closely with data scientists, platform teams, and stakeholders to design and deliver robust ingestion frameworks, feature pipelines, and deployment-ready patterns — with a strong emphasis on performance, observability, and engineering excellence.

Job Responsibility:

Data ingestion & pipeline engineering: Architect, build, and operate scalable ingestion pipelines across structured and unstructured sources (tabular, text, documents, audio, images) for batch and streaming use cases
Implement reliable transformation and storage patterns for analytics + modeling at scale (e.g., curated layers, reusable datasets, feature-ready tables)
Establish strong data quality, validation, lineage, and auditability (data contracts, schema evolution, SLAs/SLOs)
GCP-native delivery (preferred): Design and implement pipelines using GCP services such as BigQuery, GCS, Composer (Airflow), Dataflow, Pub/Sub, Dataproc, and related ecosystem tools
Support cloud migration of data/pipeline workloads from on‑prem to GCP, including environment onboarding, access patterns, and operational readiness
ML engineering: Build and standardize reusable components for feature engineering, feature stores, training/inference data pipelines, and deployment integration
Enable CI/CD for data and ML pipelines (testing, packaging, versioning, release strategy, rollbacks)
GenAI engineering & evaluation (LLMOps): Implement evaluation harnesses for GenAI systems (offline + online), including dataset management, regression testing, metric dashboards, and experiment tracking
Apply evaluation techniques such as rubrics, LLM-as-judge, computation-based metrics and custom metrics to assess response quality, groundedness, safety, and instruction following. Integrate GenAI evaluation workflows using Vertex AI GenAI evaluation service and/or custom frameworks as appropriate
Performance & reliability: Lead performance optimization across pipeline runtime, cost, throughput, and scalability (partitioning, clustering, caching, parallelization, Spark tuning, query optimization)
Build production-grade observability: monitoring, alerting, logging, tracing, and runbooks
Technical leadership: Provide senior technical leadership through design reviews, code reviews, best practices, and mentorship of junior engineers
Influence architecture and standards across teams
partner early in project scoping to recommend the right target-state pipeline patterns

Requirements:

B.S/B.Tech/B.E. degree or higher in a quantitative field such as computer sciences, applied math, engineering
Strong hands-on engineering experience building data ingestion and processing pipelines at scale (batch + streaming)
Strong data science and ML engineering background preferred
Advanced proficiency in Python and SQL, and at least one distributed processing framework (Spark preferred)
Strong experience with GCP data and orchestration services (BigQuery, GCS, Composer/Airflow
Dataflow/PubSub a strong plus)
Experience building production engineering practices: CI/CD, automated testing, monitoring/alerting, and operational support
Proven ability to collaborate with data science + platform teams and drive delivery across ambiguity
Ability to interact with both business and technology partners on tech migration/adoption

Nice to have:

Experience with Vertex AI Pipelines, feature stores, or ML workflow orchestration
familiarity with CI/CD + automation in ML systems
Experience with GenAI systems such as RAG ingestion pipelines, chunking/indexing strategies, prompt/tool orchestration, and evaluation/guardrails
Familiarity with AI/ML modeling frameworks and modeling techniques
Experience in deploying Machine Learning as-a-service using REST API’s, Flask, Django, etc
Experience with elastic search, knowledge graph good to have
Experience working in regulated environments where data governance, access controls, and auditability matter
Good to have certifications in Data Science, Data Engineering, ML Ops, Cloud services
Google Professional Cloud architect, Google AI/ML Certification

Additional Information:

Job Posted:
March 22, 2026

Expiration:
April 07, 2026

Employment Type:

Fulltime

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