Machine Learning & Data Scientist

New York City, NY - USA

Monthly Salary: Not Disclosed

Posted on: 2 hours ago

Vacancies: 1 Vacancy

Job Summary

Key Responsibilities

Machine Learning Engineering

Design develop and deploy scalable machine learning models using modern frameworks (e.g. PyTorch)
Re-engineer and optimize legacy models into efficient production-grade implementations
Improve model performance scalability and reproducibility
Support model validation benchmarking and certification processes
Ensure full traceability and documentation of model logic and outputs

Data Platform & Pipeline Engineering

Design and optimize distributed data pipelines using Spark-based platforms (e.g. Databricks)
Build and refactor ETL/ELT workflows for performance and scalability
Implement data models within modern cloud data warehouses (e.g. Snowflake)
Apply best practices for cloud-native data architecture
Standardize reusable utilities and frameworks for analytics workflows

Cloud Migration & Modernization

Participate in migration of on-prem or legacy analytics platforms to cloud ecosystems
Refactor existing codebases to align with modern engineering and DevOps standards
Leverage cloud compute capabilities (including GPU acceleration where applicable)
Support scheduling and orchestration of data and ML workflows

Testing Validation & Governance

Conduct rigorous testing and validation to ensure data and model accuracy
Perform parallel runs and benchmarking when modernizing systems
Collaborate with governance risk and compliance stakeholders
Maintain high standards of documentation and reproducibility

Required Qualifications

Technical Skills

Strong programming skills in Python
Hands-on experience with PyTorch (or similar deep learning frameworks)
Expertise in Spark-based data processing (Databricks preferred)
Strong SQL skills
Experience working with cloud data warehouses such as Snowflake
Experience building and optimizing ETL/ELT pipelines
Familiarity with distributed computing and performance tuning

Cloud & DevOps

Experience working in cloud environments (AWS Azure or GCP)
Understanding of workflow orchestration tools (e.g. Airflow native platform schedulers)
Version control and CI/CD practices for ML pipelines
Exposure to containerization and scalable deployment patterns

Preferred Qualifications

Experience modernizing legacy codebases (C R or similar)
Experience in regulated industries (Financial Services Banking Insurance etc.)
GPU optimization experience
Knowledge of model risk management or model validation frameworks
Experience supporting large-scale data transformation initiatives

Key Responsibilities Machine Learning Engineering Design develop and deploy scalable machine learning models using modern frameworks (e.g. PyTorch) Re-engineer and optimize legacy models into efficient production-grade implementations Improve model performance scalability and reproducibility Suppor...