Widewalls Ltd

Compensation

Full Description

Data Scientist Who we’re looking for Widewail is hiring a data-focused engineer to help build the next generation of our AI-driven data products. This role is within our existing data team and plays a central part in shaping how we extract insight from multiple correlated data sets. We are open to strong data scientists with solid engineering experience or hybrid ML engineers who can work comfortably across model development, data pipelines, and AI systems. The role can be remote or on-site in Burlington, VT. What you will do Work with engineering and product teams to design and build pipelines that support both traditional ML models and emerging AI workflows. Develop and deploy NLP, LLM, and machine learning models that power Widewail’s new data products. Experiment with agentic flows, retrieval architectures, and modern AI toolchains across AWS Bedrock, OpenAI, and other providers. You will explore and analyze data from multiple sources to uncover meaningful insights, identify customer-facing opportunities, and help translate raw signals into reliable, scalable models and features. You will also collaborate with engineering to move prototypes into production environments that support real customer usage. Core responsibilities Have hands-on experience with modern AI stacks, including LLMs, embeddings, RAG pipelines, vector search, and model fine-tuning. Understand NLP deeply enough to apply it in production environments, including summarization, sentiment extraction, topic modeling, and classification. Be comfortable training and evaluating ML models using methods like BLEU, F1-score, and AUC. Have experience developing or supporting AI pipelines on cloud platforms, ideally AWS, including services like Bedrock, Sagemaker, Lambda, or similar. Be able to move between data exploration and engineering with ease, writing the code needed to transform, prepare, or orchestrate data for modeling. Communicate clearly about model behavior, limitations, and tradeoffs to technical and non-technical partners. Work collaboratively to build an environment where experimentation leads to practical, deployable outcomes. Basic qualifications Two or more years in a full-time ML, data science, or applied AI role after graduation. Strong SQL skills. Strong foundation in statistics, machine learning, and data modeling. Experience developing, evaluating, and iterating on ML models. Experience with Python for modeling and data transformation. Preferred qualifications Experience with modern AI tooling such as LLM orchestration frameworks, agentic pipelines, or retrieval-augmented systems. Experience with AWS AI and ML services, including Bedrock or Sagemaker. Experience building and supporting production-grade model pipelines.