Data Science

• Data Science
• 📍 Location: New York, NY, US
• 🏢 Industry: IT Services and IT Consulting
• 💼 Work Setting: Hybrid

• Are you passionate about advancing Generative AI, Large Language Models (LLMs), Natural Language Processing, and machine learning solutions that deliver meaningful customer impact at scale?
• We are seeking a Senior Data Scientist to develop and deploy cutting-edge AI and machine learning solutions that power customer-facing products and enterprise capabilities. This role combines advanced research, model development, production deployment, and cross-functional leadership to deliver scalable AI solutions leveraging modern cloud infrastructure and open-source technologies.
• The ideal candidate brings expertise in Generative AI, LLMs, NLP, Deep Learning, Machine Learning, AWS, Python, and large-scale model deployment, along with a passion for innovation, mentoring, and solving complex business challenges.

• Key Responsibilities
• Generative AI & Large Language Models
• Develop, fine-tune, and optimize Large Language Models (LLMs) for customer-facing applications.
• Build innovative AI-powered products and features.
• Research emerging AI technologies and evaluate their practical applications.
• Design scalable solutions leveraging state-of-the-art language models.
• Improve model performance, accuracy, safety, and reliability.
• Areas of Focus
• Generative AI
• Large Language Models (LLMs)
• Foundation Models
• Prompt Engineering
• Model Fine-Tuning
• AI Innovation
• Natural Language Processing (NLP)
• Serve as a subject matter expert in NLP technologies.
• Design and develop solutions for text understanding and generation.
• Build advanced language intelligence capabilities.
• Improve customer experiences through conversational AI and language models.
• Support NLP model evaluation and optimization.
• Expertise Areas
• Natural Language Processing
• Text Analytics
• Semantic Search
• Information Extraction
• Conversational AI
• Machine Learning Development
• Build machine learning models throughout the complete lifecycle:
• Design
• Training
• Evaluation
• Validation
• Deployment
• Monitoring
• Develop predictive and intelligent systems that solve business problems.
• Ensure models are robust, explainable, and production-ready.
• Support model governance and compliance processes.
• AI Product Development
• Partner with Product Managers, Engineers, and Data Scientists to deliver AI-powered products.
• Translate business requirements into scalable machine learning solutions.
• Drive innovation that improves customer experiences and operational efficiency.
• Support end-to-end product delivery from concept through production.
• Production AI Systems
• Collaborate with engineering teams to operationalize AI models.
• Build resilient and scalable production deployments.
• Support inference platforms capable of serving large customer populations.
• Improve performance, reliability, and monitoring of deployed AI systems.
• Responsibilities
• MLOps
• Model Deployment
• Inference Optimization
• Production Monitoring
• Model Lifecycle Management
• Data Science & Analytics
• Analyze large structured and unstructured datasets.
• Extract insights from text and numerical data.
• Build analytical frameworks and experimentation approaches.
• Support strategic and data-driven decision making.
• Deliver actionable business recommendations.
• Research & Innovation
• Stay current with advancements in:
• Artificial Intelligence
• Deep Learning
• NLP
• Generative AI
• Machine Learning
• Evaluate emerging methodologies and technologies.
• Apply cutting-edge techniques to real-world business challenges.
• Drive continuous improvement of AI capabilities.
• Cross-Functional Leadership
• Collaborate closely with:
• Product Managers
• Data Scientists
• Machine Learning Engineers
• Software Engineers
• Business Stakeholders
• Executive Leadership
• Responsibilities
• Translate complex technical concepts into business outcomes.
• Influence AI strategy and roadmap decisions.
• Drive alignment across technical and business teams.
• Champion AI adoption and innovation initiatives.
• Mentorship & Technical Leadership
• Mentor junior data scientists and machine learning practitioners.
• Establish technical best practices.
• Review models, methodologies, and implementation strategies.
• Support talent development and team growth.
• Guide teams through complex AI initiatives.

Qualifications

• Education
• Required
• One of the following:
• Bachelor's Degree in:
• Statistics
• Mathematics
• Economics
• Analytics
• Operations Research
• Computer Science
• Related Quantitative Field
• plus 6 years of data analytics experience
• OR
• Master's Degree (or MBA with quantitative concentration)
• plus 4 years of data analytics experience
• OR
• PhD in a quantitative field
• plus 1 year of data analytics experience
• Preferred Education
• PhD in a STEM field:
• Computer Science
• Data Science
• Mathematics
• Statistics
• Engineering
• Related Discipline
• Technical Skills
• Artificial Intelligence & Machine Learning
• Required
• Machine Learning
• Deep Learning
• Model Training
• Model Evaluation
• Predictive Modeling
• Preferred
• Generative AI
• Large Language Models (LLMs)
• Foundation Models
• Reinforcement Learning from Human Feedback (RLHF)
• Explainable AI (XAI)
• Self-Supervised Learning
• Programming Languages
• Required
• Experience with:
• Python
• Scala
• R
• Applications
• Data Analysis
• Model Development
• AI Research
• Production ML Systems
• Databases & Analytics
• Required
• SQL
• Relational Databases
• Data Querying
• Large-Scale Analytics
• Cloud & Infrastructure
• Preferred
• AWS
• Distributed Computing
• Large-Scale Training Infrastructure
• Technologies
• AWS UltraClusters
• Cloud-Based ML Platforms
• High-Performance Compute Environments
• AI & ML Frameworks
• Experience with:
• PyTorch
• Hugging Face
• LangChain
• Lightning
• Preferred
• Vector Databases
• LLM Tooling
• Retrieval-Augmented Generation (RAG)

Experience

• Required
• Experience leveraging open-source programming languages for large-scale data analysis.
• Experience developing machine learning solutions.
• Experience working with relational databases.
• Experience building customer-facing AI products.
• Preferred
• 4+ years of machine learning experience.
• 4+ years of AI modeling experience.
• Experience deploying models into production environments.
• Experience navigating model risk and governance processes.
• Leadership Competencies
• Technical Leadership
• Strategic Thinking
• Innovation
• Talent Development
• Cross-Functional Influence
• Stakeholder Management
• Decision Making
• Professional Competencies
• Problem Solving
• Research Mindset
• Creativity
• Communication Skills
• Collaboration
• Customer Focus
• Analytical Thinking
• Continuous Learning

• Core Competencies
• Generative AI
• Large Language Models (LLMs)
• Machine Learning
• Deep Learning
• NLP
• PyTorch
• Hugging Face
• LangChain
• Python
• SQL
• AWS
• Vector Databases
• MLOps
• Predictive Analytics
• Model Deployment
• RLHF
• Explainable AI
• Data Science
• Artificial Intelligence
• Customer-Facing AI Products