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Analytics Solution Associate Senior

ExperiencedNo visa sponsorship
J.P. Morgan logo

at J.P. Morgan

Bulge Bracket Investment Banks

Posted 7 days ago

No clicks

**Analytics Solution Associate Senior** Lead end-to-end delivery of analytics data products and AI/ML solutions in Consumer & Community Banking. Define KPIs, design batch/streaming pipelines, ensure data quality, support MLOps, and collaborate with cross-functional teams. This senior role demands 4+ years of experience in data analytics, data engineering, or ML/AI. Proficiency in SQL, Python, Tableau, and Snowflake is required, along with knowledge of modern data platforms and ML lifecycles. Familiarity with MLops tooling, NLP/GenAI, and banking analytics domains is preferred.

Compensation
Not specified

Currency: Not specified

City
Bengaluru
Country
India

Full Job Description

Location: Bengaluru, Karnataka, India

 

Deliver analytics data products & AI/ML solutions end-to-end: define KPIs, build pipelines, ensure quality, support MLOps & controls.

As an Analytics Solution Associate in Consumer & Community Banking, you will support the delivery of analytics data products and applied AI/ML solutions. You will contribute across the delivery lifecycleproblem definition, requirements, data engineering, basic model development, production support, and documentationwhile partnering with Product, Technology, Data Governance, and Risk/Controls. 

 

Job Responsibilities

  • Support stakeholders in translating business needs into clear problem statements, KPIs, and success metrics (e.g., servicing insights, customer engagement, marketing measurement, forecasting, anomaly detection).
  • Assist in maintaining delivery hygiene: action items, RAID logs, dependency tracking, and status updates.
  • Build and enhance batch data pipelines (and support streaming where applicable) to produce curated, trusted datasets under guidance.
  • Help develop analytics-ready layers (data marts/semantic views) with consistent metric definitions and documentation (data dictionary, lineage notes, runbooks).
  • Implement and run data quality checks (tests, reconciliations, completeness/timeliness checks) and support monitoring/alerting and SLA tracking.
  • Contribute to ML use cases (e.g., propensity/segmentation, next-best-action components, forecasting, anomaly detection, basic NLP for servicing insights) under senior oversight.
  • Support feature engineering, model training, and evaluation, and document assumptions/limitations; follow guidance on leakage checks and bias/fairness considerations.
  • Follow established MLOps practices: version control, reproducible runs, basic automated tests, monitoring inputs/outputs, and supporting retraining/rollback procedures.
  • Adhere to security and data governance expectations (least-privilege access, sensitive data handling, retention, auditability).
  • Produce required delivery and control artifacts (documentation, traceability, operational procedures) and support control reviews/audits.
  • Apply SDLC practices: participate in code reviews, follow CI/CD patterns, support environment promotion, and assist with incident triage/root-cause analysis.

 

Required Qualifications, Capabilities, and Skills

  • 4+ years (or equivalent) experience in data analytics, data engineering, or ML/AI delivery.
  • Working proficiency in SQL and comfort with data modeling fundamentals (tables, joins, dimensional concepts).
  • Hands-on experience building analyses and dashboards in Tableau, and working with data in Snowflake (e.g., querying, validating datasets, supporting curated views).
  • Working proficiency in Python (or similar) for data processing and basic ML workflows.
  • Exposure to modern data platforms (warehouse/lakehouse concepts) and orchestration tools.
  • Basic understanding of the applied ML lifecycle (framing  build  deploy  monitor) and common evaluation metrics.
  • Comfort operating in a controlled environment (change management, access controls, documentation expectations).
  • Strong communication skills and ability to work effectively across teams with guidance.

 

Preferred Qualifications, Capabilities, and Skills

  • Exposure to consumer banking/retail analytics domains (servicing, digital, customer insights).
  • Familiarity with MLOps tooling (experiment tracking/model registry, automated pipelines, monitoring concepts).
  • Exposure to NLP/GenAI use cases in a governed enterprise environment.
  • Awareness of streaming/CDC/event-driven patterns for near-real-time analytics or detection use cases.

 

Deliver analytics data products & AI/ML solutions end-to-end

Analytics Solution Associate Senior

Compensation

Not specified

City: Bengaluru

Country: India

J.P. Morgan logo
Bulge Bracket Investment Banks

7 days ago

No clicks

at J.P. Morgan

ExperiencedNo visa sponsorship

**Analytics Solution Associate Senior** Lead end-to-end delivery of analytics data products and AI/ML solutions in Consumer & Community Banking. Define KPIs, design batch/streaming pipelines, ensure data quality, support MLOps, and collaborate with cross-functional teams. This senior role demands 4+ years of experience in data analytics, data engineering, or ML/AI. Proficiency in SQL, Python, Tableau, and Snowflake is required, along with knowledge of modern data platforms and ML lifecycles. Familiarity with MLops tooling, NLP/GenAI, and banking analytics domains is preferred.

Full Job Description

Location: Bengaluru, Karnataka, India

 

Deliver analytics data products & AI/ML solutions end-to-end: define KPIs, build pipelines, ensure quality, support MLOps & controls.

As an Analytics Solution Associate in Consumer & Community Banking, you will support the delivery of analytics data products and applied AI/ML solutions. You will contribute across the delivery lifecycleproblem definition, requirements, data engineering, basic model development, production support, and documentationwhile partnering with Product, Technology, Data Governance, and Risk/Controls. 

 

Job Responsibilities

  • Support stakeholders in translating business needs into clear problem statements, KPIs, and success metrics (e.g., servicing insights, customer engagement, marketing measurement, forecasting, anomaly detection).
  • Assist in maintaining delivery hygiene: action items, RAID logs, dependency tracking, and status updates.
  • Build and enhance batch data pipelines (and support streaming where applicable) to produce curated, trusted datasets under guidance.
  • Help develop analytics-ready layers (data marts/semantic views) with consistent metric definitions and documentation (data dictionary, lineage notes, runbooks).
  • Implement and run data quality checks (tests, reconciliations, completeness/timeliness checks) and support monitoring/alerting and SLA tracking.
  • Contribute to ML use cases (e.g., propensity/segmentation, next-best-action components, forecasting, anomaly detection, basic NLP for servicing insights) under senior oversight.
  • Support feature engineering, model training, and evaluation, and document assumptions/limitations; follow guidance on leakage checks and bias/fairness considerations.
  • Follow established MLOps practices: version control, reproducible runs, basic automated tests, monitoring inputs/outputs, and supporting retraining/rollback procedures.
  • Adhere to security and data governance expectations (least-privilege access, sensitive data handling, retention, auditability).
  • Produce required delivery and control artifacts (documentation, traceability, operational procedures) and support control reviews/audits.
  • Apply SDLC practices: participate in code reviews, follow CI/CD patterns, support environment promotion, and assist with incident triage/root-cause analysis.

 

Required Qualifications, Capabilities, and Skills

  • 4+ years (or equivalent) experience in data analytics, data engineering, or ML/AI delivery.
  • Working proficiency in SQL and comfort with data modeling fundamentals (tables, joins, dimensional concepts).
  • Hands-on experience building analyses and dashboards in Tableau, and working with data in Snowflake (e.g., querying, validating datasets, supporting curated views).
  • Working proficiency in Python (or similar) for data processing and basic ML workflows.
  • Exposure to modern data platforms (warehouse/lakehouse concepts) and orchestration tools.
  • Basic understanding of the applied ML lifecycle (framing  build  deploy  monitor) and common evaluation metrics.
  • Comfort operating in a controlled environment (change management, access controls, documentation expectations).
  • Strong communication skills and ability to work effectively across teams with guidance.

 

Preferred Qualifications, Capabilities, and Skills

  • Exposure to consumer banking/retail analytics domains (servicing, digital, customer insights).
  • Familiarity with MLOps tooling (experiment tracking/model registry, automated pipelines, monitoring concepts).
  • Exposure to NLP/GenAI use cases in a governed enterprise environment.
  • Awareness of streaming/CDC/event-driven patterns for near-real-time analytics or detection use cases.

 

Deliver analytics data products & AI/ML solutions end-to-end