Job Description
The Specialized Analytics Ld Analyst is a strategic professional who stays abreast of developments within own field and contributes to directional strategy by considering their application in own job and the business. Recognized technical authority for an area within the business. Requires basic commercial awareness. There are typically multiple people within the business that provide the same level of subject matter expertise. Developed communication and diplomacy skills are required in order to guide, influence and convince others, in particular colleagues in other areas and occasional external customers. Significant impact on the area through complex deliverables. Provides advice and counsel related to the technology or operations of the business. Work impacts an entire area, which eventually affects the overall performance and effectiveness of the sub-function/job family.
Responsibilities:
Performing data extraction, manipulation and processing, explore new data sources and integrate into pipeline
Effectively using programming languages and tools (e.g., Python, PySpark, SAS, SQL, Excel)
Normalize data to perform design of experiments; analyze and finalise data in required structure; design appropriate dashboards
prompt engineering and large language models, Cypher Query, User Stories based few-shot modeling, Ground Truth verification, RAG, Knowledge Graphs, Guardrail models
Incumbents in this role may often be referred to as Data Scientists
Clustering and segmentation
Classification and regression techniques
Ensemble modelling/advanced machine learning models, Natural Language Processing
P&L quantification of financial impact of models implemented in the market through CRM efforts
Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients and assets, by driving compliance with applicable laws, rules and regulations, adhering to Policy, applying sound ethical judgment regarding personal behavior, conduct and business practices, and escalating, managing and reporting control issues with transparency.
Qualifications:
8 - 14 years of experience (We have multiple roles at AVP/VP level)
Experience in a quantitative field, working hands-on inData Analysis, Statistics, Machine Learning and Feature Engineering
Must have Demonstrated ability inAI: Gen AI: prompt engineering and large language models,Cypher Query, User Stories based few-shot modelling, Ground Truth verification, RAG, Knowledge Graphs, Guardrail models,Optical Charter Recognition (OCR)/ computer vision, Deep learning and neural networks
Excellent analytic ability and problem solving skills
Proficient inMicrosoft Office includingexcellent MS Excel skills and Microsoft Powerpoint to develop analytic presentations
Excellent communication and interpersonal skills, be organized, detail oriented, and adaptive to matrix work environment
Stakeholder Management: Stakeholder mapping; identifying who to engage for responsibilities and actions; Interpersonal ability; building relationships virtually ; Influencing, negotiation, alignment on priorities/incentives and conflict resolution with stakeholders; stakeholder communication
Project Management: Prioritization; resource planning, timelines management and escalations, governance on projects; project status communications; running steercos; Navigating through constantly evolving timelines and project scope including
Data Engineering: Manipulating data in a distributed system natively (Hadoop Clusters) as well as on cloud (AWS Clusters). MLOPS platforms like 'MLflow', 'AWS Sagemaker' and 'Vertex AI'. Front end integration: Python frameworks like Streamlit and Flask or R frameworks like R Shiny etc., JavaScript (preferred but not mandatory), bootstrap (preferred but not mandatory) and CSS (preferred but not mandatory)
Education:
Bachelors/University degree, Master's degree preferred
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Job Family Group:
Decision Management------------------------------------------------------
Job Family:
Specialized Analytics (Data Science/Computational Statistics)------------------------------------------------------
Time Type:
Full time------------------------------------------------------
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