Data Scientist

About the role

Data Scientist

📍 Location: New York, NY, US

🏢 Industry: Financial Services

💼 Work Setting: Hybrid

Are you a hands-on Data Scientist who enjoys building machine learning solutions, extracting insights from large datasets, and turning advanced analytics into business value?

This role partners with Data Scientists, Software Engineers, Product Managers, and business stakeholders to design, develop, validate, and deploy machine learning solutions that solve real-world customer and business problems. The position combines expertise in machine learning, statistical modeling, cloud computing, big data analytics, and data engineering to deliver scalable, production-ready AI solutions.

The ideal candidate has a strong foundation in statistics, machine learning, Python, SQL, cloud platforms, and big data technologies and thrives in solving complex, ambiguous problems.

Key Responsibilities

Machine Learning Model Development

Build machine learning models from concept to production.

Design, train, evaluate, validate, and deploy predictive models.

Apply statistical and machine learning techniques to solve business problems.

Improve model performance through experimentation and optimization.

Develop scalable and reusable machine learning solutions.

Areas of Focus

Classification

Regression

Clustering

Time Series Forecasting

Sentiment Analysis

Deep Learning

Predictive Analytics

Advanced Analytics & Data Science

Analyze large-scale structured and unstructured datasets.

Extract actionable business insights from complex data.

Perform exploratory data analysis (EDA).

Identify trends, patterns, and customer behaviors.

Translate analytical findings into business recommendations.

Big Data & Data Engineering

Work with large volumes of numerical and textual data.

Build scalable data preparation and feature engineering pipelines.

Retrieve, combine, and transform data from multiple sources.

Improve data quality and analytical readiness.

Support machine learning workflows with efficient datasets.

Key Activities

Data Wrangling

Feature Engineering

Data Integration

Data Transformation

Data Preparation

Cloud-Based Data Science

Develop and deploy machine learning solutions on cloud platforms.

Leverage cloud-native data science tools and services.

Build scalable analytical environments.

Support production machine learning workloads.

Technologies

AWS

Cloud Analytics Services

Scalable Compute Platforms

Machine Learning Lifecycle Management

Support the complete machine learning lifecycle:

Problem Definition

Data Collection

Feature Engineering

Model Development

Validation

Deployment

Monitoring

Continuously improve models through testing and iteration.

Ensure models remain accurate and reliable in production.

Cross-Functional Collaboration

Partner closely with:

Product Managers

Data Scientists

Software Engineers

Business Stakeholders

Analytics Teams

Responsibilities

Understand customer and business needs.

Translate technical insights into business outcomes.

Explain complex analytical concepts in an understandable way.

Support data-driven product development.

Research & Innovation

Stay current on emerging machine learning techniques and technologies.

Evaluate new tools, methodologies, and frameworks.

Apply innovative approaches to business challenges.

Contribute to continuous improvement of analytics capabilities.

Qualifications

Education

Required

One of the following:

Option 1

Bachelor's Degree in a quantitative discipline such as:

Statistics

Economics

Mathematics

Analytics

Operations Research

Computer Science

Plus:

5+ years of data analytics experience.

Option 2

Master's Degree in a quantitative discipline or MBA with quantitative concentration.

Plus:

3+ years of data analytics experience.

Option 3

PhD in a quantitative discipline.

Preferred Education

Master's Degree in STEM field with 3+ years of experience.

PhD in STEM field.

Technical Skills

Programming

Required

Python

SQL

Preferred

Scala

R

Machine Learning

Required

Statistical Modeling

Supervised Learning

Unsupervised Learning

Model Evaluation

Predictive Analytics

Preferred

Deep Learning

Advanced ML Techniques

Production ML Systems

Cloud Platforms

Preferred

AWS

Experience deploying and supporting analytics solutions in cloud environments.

Big Data Technologies

Experience working with technologies such as:

Spark

H2O

Distributed Analytics Platforms

Large-Scale Data Processing Systems

Data Analysis & Statistics

Strong knowledge of:

Hypothesis Testing

Model Validation

Backtesting

Statistical Inference

Performance Measurement

Familiarity With

ROC Curves

Confusion Matrices

Precision & Recall Metrics

Model Accuracy Evaluation

Professional Competencies

Analytical Skills

Critical Thinking

Problem Solving

Statistical Reasoning

Quantitative Analysis

Data Interpretation

Communication Skills

Technical Communication

Business Storytelling

Stakeholder Engagement

Presentation Skills

Cross-Functional Collaboration

Leadership & Innovation

Research Mindset

Curiosity

Innovation

Customer Focus

Continuous Learning

Core Competencies

Data Science

Machine Learning

Predictive Analytics

Statistical Modeling

Python

SQL

AWS

Spark

H2O

Deep Learning

Sentiment Analysis

Clustering

Classification

Time Series Analysis

Big Data Analytics

Feature Engineering

Data Visualization

Business Analytics

Data Engineering