Data Scientist

Stack

Responsibilities

• ▹Develop, evaluate and improve machine learning and generative AI solutions that power Intermedia's Digital Agent Platform
• ▹Apply large language models (LLMs), natural language processing, retrieval and other advanced AI techniques to improve agent understanding and performance
• ▹Develop approaches that improve agent reasoning, tool selection, knowledge retrieval, context management, personalization and task completion
• ▹Experiment with model, prompt, retrieval and agent configuration strategies to identify approaches that deliver the best customer and business outcomes
• ▹Evaluate commercial and open-source models and recommend appropriate approaches based on quality, latency, scalability and cost
• ▹Design, develop and optimize Retrieval-Augmented Generation (RAG) solutions that ground digital agents in relevant enterprise and customer information
• ▹Develop and evaluate embeddings, retrieval strategies, ranking approaches, semantic search and other knowledge-retrieval techniques
• ▹Build and refine end-to-end pipelines that combine LLMs with retrieval systems, enterprise knowledge sources and agent workflows
• ▹Develop approaches to improve the relevance, accuracy and consistency of AI-generated responses
• ▹Identify and mitigate issues such as hallucinations, poor retrieval, inappropriate responses and other failure modes in generative AI applications
• ▹Develop rigorous evaluation frameworks for measuring digital agent quality, including accuracy, relevance, task completion, reliability, safety and customer experience
• ▹Design offline and online experiments to compare models, prompts, retrieval strategies, agent configurations and other AI approaches
• ▹Apply statistical analysis, hypothesis testing, segmentation and other quantitative methods to evaluate AI performance and identify opportunities for improvement
• ▹Define appropriate metrics and benchmarks that connect model and agent performance to customer and business outcomes
• ▹Analyze production behavior and feedback to identify patterns, failure modes and opportunities to continuously improve digital agents
• ▹Ensure data and model quality through comprehensive testing, validation and performance evaluation
• ▹Gather, preprocess, analyze and model large volumes of structured and unstructured data from multiple sources
• ▹Use Python, SQL, Spark and other relevant technologies to build scalable analytical and machine learning solutions
• ▹Develop features, datasets and analytical approaches that support model development, experimentation and evaluation
• ▹Partner with data and engineering teams to build and maintain reliable pipelines for model training, evaluation and production use
• ▹Contribute to scalable ML/AI pipelines that support experimentation, deployment, monitoring and continuous improvement
• ▹Ensure solutions are reproducible, maintainable and designed to operate effectively at production scale
• ▹Partner with Product, AI/ML Engineering, Software Engineering and other Data Science team members to translate customer and business problems into effective AI solutions
• ▹Communicate analytical findings, model performance, tradeoffs and recommendations clearly to technical and non-technical stakeholders
• ▹Participate in technical and design reviews and help establish strong practices for AI experimentation, evaluation and model development
• ▹Mentor and support less experienced data scientists and contribute to knowledge sharing across the team

Requirements

• ▹Bachelor's degree in Data Science, Statistics, Computer Science, Mathematics, Machine Learning, Analytics or a related quantitative field; Master's degree preferred
• ▹4+ years of professional experience in data science, machine learning, applied AI or a related analytical field
• ▹Strong experience developing and applying supervised and unsupervised machine learning models to real-world problems
• ▹Hands-on experience with generative AI, large language models, NLP or other modern AI technologies
• ▹Strong programming skills in Python and experience with SQL and large-scale data processing technologies
• ▹Experience with modern machine learning frameworks such as PyTorch, TensorFlow, scikit-learn or equivalent technologies
• ▹Strong understanding of statistical analysis, experimentation, hypothesis testing, model evaluation and performance measurement
• ▹Experience working with large volumes of structured and unstructured data
• ▹Demonstrated ability to independently frame ambiguous problems, develop analytical approaches, evaluate alternatives and deliver actionable solutions
• ▹Experience taking machine learning or AI solutions beyond experimentation and contributing to their deployment and operation in production environments
• ▹Understanding of model quality, data quality, bias, reliability and other considerations associated with production AI systems
• ▹Strong problem-solving and analytical skills with the ability to connect technical results to customer and business outcomes
• ▹Strong written and verbal communication skills with the ability to explain complex analytical and AI concepts to technical and non-technical audiences
• ▹Ability to collaborate effectively across Data Science, AI/ML Engineering, Software Engineering, Product and business teams

Soft skills

About the company

Intermedia is a leading provider of cloud communications and collaboration technology.