Data Scientist

Skai-posted about 2 months ago

$130,000 - $145,000/Yr

Full-time • Mid Level

Hybrid • New York, NY

501-1,000 employees

Professional, Scientific, and Technical Services

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Ready to take your career to the next level? Skai (formally Kenshoo) is looking for the best and the brightest to join our rapidly growing team. We're proud of our industry-leading digital marketing software but we're even prouder of the people behind it. That's where you come in! Data Scientist (Mid-Level) We are seeking a mid-level Data Scientist with expertise in Natural Language Processing and Generative AI who can deliver production-quality code and solutions. In this role, you will design and implement AI-driven features in the marketing and e-commerce domain, taking prototypes through to scalable production applications. The ideal candidate has hands-on experience with large language models (LLMs), AI agents, and modern machine learning practices, and can effectively bridge the gap between experimental proof-of-concepts and robust, real-world systems.

AI Use Case & Prototyping: Identify opportunities for generative AI applications and develop proof-of-concept solutions using LLMs and prompt engineering. Build and test early-stage prototypes (e.g., automatic text summarization, recommendation engines) and outline requirements for user interfaces and AI agents that interact with these models.
Experimentation & Feature Development: Design and run A/B and other experiments to validate new features. Analyze marketing and e-commerce data to uncover product improvement insights and address client pain points. Develop data-driven solutions (machine learning models or statistical analyses) to enhance product performance and user experience.
Data Analysis & Client Support: Handle ad-hoc business data requests and translate analysis results into actionable insights for clients. Present data findings clearly (visualizations and reports) and support strategic initiatives (e.g., advanced marketing analytics reporting). Coordinate with external teams to integrate data insights into client deliverables.
Production Deployment & System Integration: Implement and refine generative AI prototypes into production-ready features. Ensure system stability by using robust engineering practices such as proper error handling, retries, and caching for LLM API calls. Work closely with ML engineers and architects to integrate AI solutions with external systems, ensuring scalability and reliability in a cloud environment.
Best Practices & Collaboration: Follow software development best practices - including coding standards, version control, code reviews, and unit testing - to produce maintainable, production-ready code. Collaborate effectively in cross-functional teams, assist with debugging issues in a team setting, and document processes and findings clearly for future reference.

Software Engineering & Data Proficiency: Strong Python programming skills and SQL proficiency for data manipulation and analysis. Demonstrated ability to write production-grade code (efficient, well-documented, tested). Experience working with cloud environments (AWS preferred) for data processing and model deployment.
NLP & LLM Expertise: Hands-on experience with NLP tasks (e.g. text classification, summarization) and working with large language models. Familiarity with fine-tuning models and evaluating LLM performance on various tasks. Knowledge of advanced NLP techniques like retrieval-augmented generation (RAG) and use of vector databases is a plus.
Generative AI Development: Ability to build and iterate on LLM-powered applications. Skilled in prompt engineering and using LLM APIs (e.g., OpenAI or Hugging Face endpoints) to develop prototypes. Comfortable implementing agent-based workflows - for example, leveraging frameworks like LangChain - to enable multi-step reasoning or tool integrations. Exposure to strategies for robust LLM integration, such as error handling and result caching, is highly valued.
MLOps & Deployment: Understanding of how to deploy machine learning models and AI services to production. Familiar with containerization (Docker) and continuous integration/continuous deployment (CI/CD) pipelines for ML projects. Knowledge of building APIs (e.g., using FastAPI) for model serving and experience with model monitoring/automation tools.
Statistical Analysis & Product Sense: Strong foundation in statistics and experimental design (A/B testing, regression analysis, causal inference). Able to interpret experiment results in a business context and derive actionable product insights. Displays product thinking - can connect data science outcomes to user experience and business value, especially in marketing or e-commerce scenarios.
Communication & Collaboration: Effective communicator who can explain complex AI/ML concepts to non-technical stakeholders and incorporate feedback. Strong teamwork and project management skills, with experience working in cross-functional environments (engineering, product, and client teams) to deliver AI solutions on time.
Master's degree or higher in Computer Science, Data Science, Statistics, Engineering, or a related quantitative field.
3+ years of professional experience in data science, machine learning, or applied AI (mid-level role).
Proficiency in Python and SQL for data analysis, modeling, and software development.
Practical experience with NLP and LLM-based projects, including building or fine-tuning language models and retrieval-augmented generation (RAG) complex systems.
Proven ability to design experiments and perform statistical analysis, with a strong quantitative background.
Experience deploying machine learning models or AI applications in production environments, using tools such as Docker containers and CI/CD workflows.
Excellent communication skills and experience collaborating in cross-functional teams.

Experience with cloud ML services and infrastructure (e.g., AWS SageMaker, AWS Bedrock, or similar platforms) for developing and deploying AI solutions.
Familiarity with LLM orchestration frameworks and agent-based AI systems (e.g., LangChain, LangGraph).
Knowledge of MLOps best practices, including model versioning, monitoring, and use of tools like MLflow or Weights & Biases for experiment tracking.
Domain experience in marketing technology or e-commerce - applying data science techniques to areas like customer segmentation, personalization, or marketing optimization.
Contributions to open-source projects or research publications in NLP, deep learning, or generative AI (a plus, but not required).

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