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Natalia Guseva

Natalia Guseva

Business/System Analyst – AI/ML-based Risk Assessment Platform (Argoinsurance, US)

SPAIN flag
N/A, Spain
$50.00/hrEntry Level

Key Skills

Software

No software listed

Top Subject Matter

Banking Domain Expertise
Software Project Management
Form Automation

Top Data Types

TextText
ImageImage
DocumentDocument

Top Task Types

Text Generation

Freelancer Overview

Hands-on experience supporting AI/ML initiatives from a business and data analysis perspective, with a strong focus on defining, structuring, and validating training data within complex enterprise environments. Training data definition & preparation. Elicited and formalized data requirements for ML models by working closely with domain experts (e.g., agricultural insurance SMEs), ensuring that input data is relevant, structured, and aligned with model objectives. This includes identifying critical data attributes, data sources, and business rules impacting model performance. Data analysis, mapping, and quality control. SQL and data modelling skills, analysed complex datasets, performed data mapping across systems, and ensured consistency and integrity of datasets used for ML pipelines. Played a key role in resolving data issues during integration, directly impacting model reliability. Domain modelling for AI systems. Designed domain models and structured representations of business processes to support AI solutions, including an LLM-based automation tool. This involves translating unstructured business logic into formats usable for training and inference (e.g., forms, rules, workflows).

Entry LevelRussianEnglish

Labeling Experience

Business/System Analyst – AI/ML-based Risk Assessment Platform (Argoinsurance, US)

TextText Generation
Worked on requirements definition for an AI/ML-based risk assessment and automation platform in the insurance sector. Designed a domain model supporting the development of a Large Language Model (LLM) solution for automating corrections of FSA forms. Collaborated with subject-matter experts and users to collect and synthesize domain-specific data for AI training purposes. • Defined insurance domain-specific AI/ML requirements • Created a domain model for LLM solution • Supported development of automated text correction using agentic processes • Analyzed real-world documentation to identify data and integration opportunities

Worked on requirements definition for an AI/ML-based risk assessment and automation platform in the insurance sector. Designed a domain model supporting the development of a Large Language Model (LLM) solution for automating corrections of FSA forms. Collaborated with subject-matter experts and users to collect and synthesize domain-specific data for AI training purposes. • Defined insurance domain-specific AI/ML requirements • Created a domain model for LLM solution • Supported development of automated text correction using agentic processes • Analyzed real-world documentation to identify data and integration opportunities

2023 - Present

Business/System Analyst

DocumentSegmentation
Sales & Manufacturing. Project: ML-based pricing guidance solution for Performance Resilience BU AI/ML use case identification & product shaping. Helped shape ML solutions (pricing guidance) by identifying where machine learning adds value and defining the data prerequisites needed to support those use cases effectively Bridging business requirements to ML implementation. Translated business needs into functional specifications, user stories, and acceptance criteria that guide data scientists and engineers in building ML solutions, ensuring that training datasets and features reflect real-world use cases. Data analysis, mapping, and quality control. Using SQL and data modelling skills, analysed complex datasets, performed data mapping across systems, and ensured consistency and integrity of datasets used for ML pipelines.

Sales & Manufacturing. Project: ML-based pricing guidance solution for Performance Resilience BU AI/ML use case identification & product shaping. Helped shape ML solutions (pricing guidance) by identifying where machine learning adds value and defining the data prerequisites needed to support those use cases effectively Bridging business requirements to ML implementation. Translated business needs into functional specifications, user stories, and acceptance criteria that guide data scientists and engineers in building ML solutions, ensuring that training datasets and features reflect real-world use cases. Data analysis, mapping, and quality control. Using SQL and data modelling skills, analysed complex datasets, performed data mapping across systems, and ensured consistency and integrity of datasets used for ML pipelines.

2020 - 2021

Education

P

Paris-Dauphine University

Master of Science, Audit, Control and Risk Management

Master of Science
2014 - 2015
S

Saint-Petersburg State University

Master of Science, Social Psychology

Master of Science
1994 - 1999

Work History

N

N.O.A.H.

Business/System Analyst

N/A
2023 - Present
L

Luxoft

Business/System Analyst

N/A
2021 - 2022