Senior Analyst, Specialized Analytics

Jobtailor; Town of Florida (NY); On-site; USD 120,000 - 150,000; Full time

Job summary

Jobtailor seeks an experienced data scientist to lead fraud model development and feature engineering. You will build predictive models, manage model lifecycle, and collaborate with technology, analytics, and business partners to detect and prevent fraud across the lifecycle.

Ideal candidates have 3+ years in data science, strong Python/R/SQL skills, and expertise in ETL, data pipelines, and real-time streaming. Join a dynamic team focused on rigorous data quality and regulatory practices.

Qualifications

• Bachelor’s degree in statistics, mathematics, physics, economics, or a quantitative field.
• 3+ years in data science, ML, or advanced analytics.
• Proficiency in Python, R, or SQL.
• Experience with Pandas, Numpy, PySpark.

Responsibilities

• Lead data and feature engineering for fraud model development.
• Build predictive models and ML/AI algorithms on structured and unstructured data.
• Own and manage fraud models, risk appetite execution, and defect analysis.
• Deploy ML models for real-time fraud detection and monitoring.
• Design data pipelines, ETL processes, and real-time streaming.
• Explain complex findings to tech, analytics, and business teams.
• Monitor model performance and conduct validation/testing.

Skills

Python

R

SQL

Pandas

Numpy

PySpark

ML Algorithms

Feature Engineering

ETL

Data Streaming

Education

Bachelor’s degree

Tools

None

Job description

• Lead data and feature engineering efforts for fraud model development
• Build predictive models and machine-learning and AI algorithms using structured and unstructured data
• Own and manage fraud models, risk appetite execution, and defect analysis
• Design and implement machine learning models to detect and prevent fraud across the fraud lifecycle
• Process large, complex datasets through cleaning, normalization, and augmentation
• Conduct exploratory data analysis to identify patterns, trends, and anomalies
• Collaborate with technology teams, fraud analytics, and business partners
• Optimize fraud models through feature selection, hyperparameter tuning, and performance monitoring
• Support model deployment and production integration for real-time fraud detection
• Evaluate machine learning algorithms and tools for fraud detection needs
• Participate in data quality, governance, model validation, and testing initiatives
• Generate regular and ad-hoc reporting on emerging trends

Requirements

• Bachelor’s Degree required in statistics, mathematics, physics, economics, or other analytical or quantitative discipline
• 3+ years in data science, machine learning, or advanced analytics
• Proficiency in Python, R, or SQL
• Strong experience with Pandas, Numpy, or PySpark
• Deep understanding of machine learning algorithms and statistical modeling techniques used for fraud detection
• Expertise in feature engineering
• Experience with building and optimizing data pipelines, ETL processes, and real-time data streaming
• Familiarity with model development, monitoring, and versioning in production environments
• Ability to conduct exploratory data analysis (EDA)
• Proven cross-functional collaboration with technology, analytics, and business teams
• Ability to translate complex technical findings into clear, actionable insights
• Strong problem-solving skills
• Familiarity with regulatory requirements and best practices related to fraud modeling and risk management
• Ability to manage multiple projects and priorities while meeting tight deadlines
• High level of attention to detail and precision
• Strong intellectual curiosity and eagerness to stay updated with developments in data science, machine learning, and fraud detection techniques

Core Competencies

Demonstrates expertise in building and optimizing machine learning models for fraud detection, with strong capabilities in data engineering, exploratory data analysis, and cross-functional collaboration. Proficient in Python, R, and SQL, with a solid understanding of statistical modeling techniques and regulatory requirements.

Highest-signal resume keywords

• Machine Learning Model Development
• Feature Engineering
• Data Pipeline Optimization
• Exploratory Data Analysis
• Cross-Functional Collaboration

ATS Optimization Keywords

Hard Skills

• Python
• R
• SQL
• Pandas
• Numpy
• PySpark
• Machine Learning Algorithms
• Statistical Modeling
• ETL Processes
• Data Streaming

Soft Skills

• Problem-Solving
• Attention to Detail
• Intellectual Curiosity
• Project Management
• Communication

Industry Keywords

• Fraud Detection
• Risk Management
• Data Quality
• Model Validation
• Data Governance