Intermedia Intelligent Communications

Unified communications and cloud collaboration provider

Data Scientist

Natural Language Processing & LLMsUpdated on Nov 13

Full-timeRemote in PortugalNo salary listed

About the Job

Requirements

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

Responsibilities

Develop, evaluate, and improve machine learning and generative AI solutions for the Digital Agent Platform.
Apply large language models, 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 customer and business outcomes.
Evaluate commercial and open-source models and recommend approaches based on quality, latency, scalability, and cost.
Design, develop, and optimize Retrieval-Augmented Generation solutions that ground digital agents in 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 combining large language models 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 hallucinations, poor retrieval, inappropriate responses, and other failure modes in generative AI applications.
Develop rigorous evaluation frameworks for digital agent quality, including accuracy, relevance, task completion, reliability, safety, and customer experience.
Design offline and online experiments comparing 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 improvement opportunities.
Define metrics and benchmarks connecting model and agent performance to customer and business outcomes.
Analyze production behavior and feedback to identify patterns, failure modes, and opportunities to improve digital agents continuously.
Ensure data and model quality through 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 supporting 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 machine learning and AI pipelines supporting 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 to technical and non-technical stakeholders.
Participate in technical and design reviews and help establish practices for AI experimentation, evaluation, and model development.
Mentor and support less experienced data scientists and contribute to knowledge sharing across the team.

Desired Qualifications

A master's degree is preferred.

About Intermedia

Are you looking for a company where YOUR VOICE is heard? Where can you MAKE A DIFFERENCE? Do you
THRIVE in a FAST-PACED work environment? Do you wake every morning EXCITED to work with GREAT
PEOPLE and create SUCCESS TOGETHER? Then Intermedia is the place for you.
Intermedia has established itself as a leading provider of cloud communications and collaboration tech that allows
companies to connect better. We have a strong track record of growth, profitability, and creating an environment
where everyone matters. Everyone. While we are fast-paced and admittedly a bit intense, we promise that you won’t
be bored. You will find Intermedia is a place where you can indulge your passion for creating and supporting great
cloud technology. What’s more, we always look to promote from within and have many employees who have been
with us 10, 15, and 20+ years!

Culture at Intermedia is built on teamwork and transparency. We hold each other accountable and always have each other’s back!

While primarily remote, this role requires occasional visits to the office in Coimbra or in Aveiro. We plan to open an office in Porto in the future. This approach gives team members the flexibility to work remotely while also coming together in the office for collaboration and teamwork.

About the Role

Intermedia is looking for a Data Scientist to join our AI Data Science team.

In this role, you will develop and apply advanced machine learning, generative AI, and statistical techniques to improve the intelligence, effectiveness, and reliability of Intermedia's digital agents. You will work on challenging problems involving large language models, retrieval, agent behavior, experimentation, evaluation, and optimization to help create digital agents that can understand customer needs, access relevant knowledge, and successfully complete complex tasks.

You will work closely with AI/ML engineers, software engineers, product managers, and other data scientists to take ideas from experimentation through production. You will have significant ownership of data science solutions while helping establish strong practices for measuring and continuously improving the quality of our AI-powered experiences.

Machine Learning & Agent Intelligence

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.

Generative AI & RAG

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.

Evaluation & Experimentation

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.

Data & Solution Development

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.

Collaboration

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.

About the Company

Intermedia Intelligent Communications

Unified communications and cloud collaboration provider

Intermedia provides a cloud-based unified communications and collaboration suite for small to mid-sized businesses. Its offerings include voice, video, chat, contact center, file sharing, and email services delivered through a single, subscription-based platform that customers access from the cloud. The company works by delivering an integrated set of tools via a monthly or annual plan, reducing the need for multiple vendors. It differentiates itself with award-winning technical support from JD Power and a partner program that lets resellers offer Intermedia’s services to their own customers. Its goal is to simplify business communications for SMBs by providing an all-in-one, scalable cloud solution with reliable service and support.

Company size: Not listed
Company stage: Not listed
Total funding: Not listed
Headquarters: Sunnyvale, CA
Founded: 1995

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