Data Science Sr Lead Analyst - SVP

Key Skills

Aartificial intelligence, Data Analytics, Data science techniques, Large Language Models - LLMs, Leadership Skill, Machine learning techniques, Project management

Job Description

The Data Science Sr Lead Analyst is a strategic professional who closely follows latest trends in own field and adapts them for application within own job and the business. Typically a small number of people within the business that provide the same level of expertise. Excellent communication skills required in order to negotiate internally, often at a senior level. 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. Accountable for significant direct business results or authoritative advice regarding the operations of the business. Necessitates a degree of responsibility over technical strategy. Primarily affects a sub-function. Responsible for handling staff management issues, including resource management and allocation of work within the team/project.

The candidate should be a proficient individual contributor in machine learning to help us extract value from data. Proficiency is desired in all the processes from data collection, cleaning, and preprocessing, to training models, identifying & solving problems using “Large language Models (LLMs)” on private public cloud and deploying solutions to production.

The ideal candidate should be passionate about artificial intelligence, LLMs and stay up to date with the latest & cutting-edge developments in the field.

Responsibilities:

• Work as Lead for a suite of applications for the Internal Audit team.

• Consults with business clients to determine system functional specifications. Applies comprehensive understanding of how multiple areas collectively integrate to contribute towards achieving business objectives

• Consults with users and clients to solve complex system issues/problems by conducting strategic data analysis, identifying insights and implications and making strategic recommendations, developing data displays that clearly communicating complex analysis

• Mines and analyzes data from various banking platforms to drive optimization and improve data quality

• Deliver initiatives to address business problems with the ability to identify data required, assess time & effort required and establish a project plan

• Understand requirements and code and unit test required components

• Report progress on work and work collaboratively with the existing global team

• Keep abreast of latest technological happenings in his work area and bring relevant ideas/concept to the table

• Leads system change process from requirements through implementation; provides user and operational support of application to business users

• Impacts the business directly by ensuring the quality of work provided by self and others; impacts own team and closely related work teams

• Drives communication between business leaders and IT; exhibits sound and comprehensive communication and diplomacy skills to exchange complex information

• 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:

• 12+ years experience using tools for statistical modeling of large data sets

• Process Improvement or Project Management experience

Education:

• Bachelor’s/University degree or equivalent experience, potentially Masters degree

The ideal candidate should have past experience in the following areas:

• Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress.

• Understand & analyze business problems and try and find solutions by using tools involving AI/ML, Large Language models.

• Analyzing the ML algorithms/LLMs that could be used to solve a given problem and ranking them by their success probability.

• Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world

• Verifying data quality, and/or ensuring it via data cleaning

• Supervising the data acquisition process if more data is needed

• Finding available datasets that could be used for training

• Defining validation strategies

• Defining the preprocessing or feature engineering to be done on a given dataset

• Store preprocessed data for quick lookup when applicable

• Defining data augmentation pipelines

• Training models and tuning their hyper-parameters

• Analyzing the errors of the model and designing strategies to overcome them

• Tuning models

• Capability to identify pertinent models fit for the problem at hand