Quantitative AI Analyst

Job Description

Job Description

Quantitative AI Analyst Job Description Job Title

Quantitative AI Analyst

Location

[City/Remote/Hybrid]

Employment Type

Full-time / Contract

Job Summary

We are seeking a Quantitative AI Analyst to leverage advanced analytics, statistical modeling, machine learning, and artificial intelligence to solve complex business problems and support data-driven decision-making. The ideal candidate will combine expertise in quantitative analysis, predictive modeling, and AI techniques to develop actionable insights, optimize business processes, and build intelligent analytical solutions across domains such as finance, healthcare, retail, manufacturing, and technology.

Key Responsibilities

• Analyze large, structured, and unstructured datasets to identify trends, patterns, and business opportunities.

• Develop statistical, predictive, and machine learning models to support business decisions.

• Build AI-driven forecasting, optimization, risk assessment, and recommendation models.

• Design and evaluate experiments to validate model performance and business impact.

• Develop dashboards, reports, and data visualizations to communicate analytical findings.

• Collaborate with business stakeholders to translate requirements into analytical solutions.

• Apply generative AI and large language models (LLMs) where appropriate to enhance analytics workflows.

• Perform feature engineering, data preparation, model validation, and performance monitoring.

• Support AI governance by documenting methodologies, assumptions, and model performance.

• Continuously improve models through testing, retraining, and performance optimization.

• Stay current with emerging AI, analytics, and quantitative modeling techniques.

Required Qualifications

• Bachelor's or Master's degree in Data Science, Statistics, Mathematics, Computer Science, Artificial Intelligence, Economics, Finance, Operations Research, Engineering, or a related field.

• 2 6 years of experience in quantitative analysis, data science, business analytics, AI, or machine learning.

• Strong knowledge of statistics, probability, predictive modeling, and optimization techniques.

• Proficiency in Python and SQL for data analysis and model development.

• Experience with machine learning frameworks such as Scikit-learn, TensorFlow, or PyTorch.

• Experience with data visualization tools such as Power BI, Tableau, or Matplotlib.

• Strong analytical, problem-solving, and communication skills.

Preferred Qualifications

• Experience with generative AI, LLMs, or AI-assisted analytics.

• Familiarity with cloud-based analytics and AI platforms.

• Experience in financial modeling, risk analytics, demand forecasting, or operational optimization.

• Knowledge of MLOps, model monitoring, and deployment practices.

• Relevant certifications in data analytics, AI, cloud technologies, or business intelligence.

Technical Skills

• Python

• SQL

• Statistics

• Probability

• Predictive Analytics

• Machine Learning

• Artificial Intelligence

• Regression Analysis

• Time Series Forecasting

• Optimization Techniques

• Scikit-learn

• TensorFlow

• PyTorch

• Pandas

• NumPy

• Matplotlib

• Seaborn

• Power BI

• Tableau

• Jupyter Notebook

• Large Language Models (LLMs)

• Prompt Engineering (preferred)

• Git

• REST APIs

• AWS, Microsoft Azure, or Google Cloud (preferred)

Soft Skills

• Analytical thinking

• Quantitative reasoning

• Critical thinking

• Problem-solving

• Business acumen

• Communication and presentation

• Stakeholder management

• Collaboration

• Attention to detail

• Continuous learning

Key Deliverables

• Predictive and statistical models

• AI-powered analytical solutions

• Forecasting and optimization models

• Business intelligence dashboards

• Data analysis reports

• Model validation and performance reports

• Technical documentation

• Business recommendations supported by quantitative evidence

Success Metrics

• Model accuracy and predictive performance

• Quality and timeliness of analytical insights

• Business impact from AI-driven recommendations

• Improvement in forecasting and decision-making accuracy

• Stakeholder adoption of analytical solutions

• Reduction in operational risk and inefficiencies

• Compliance with data governance and model documentation standards

• On-time delivery of analytics and AI initiatives