Data Scientist

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.

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.