Data Scientist | Artifical Intelligence

Data scientists on FTI's AI Build Team should have a pragmatic approach to problem‑solving and be capable of developing end‑to‑end solutions that clients can rely on in production. They should be capable of challenging their own work and finding a balance between perfection and practicality. The AI build team is technology‑agnostic with software engineering expertise to deploy advanced ML/AI methodologies at scale. Engaged work includes NLP for automated document classification, geospatial modeling of electric vehicle demand, demand forecasting, predictive maintenance, price optimization, and developing and maintaining production‑grade agentic RAG systems that serve the entire firm.

As a Data Scientist, you will research solutions and deliver high‑quality outcomes with minimal guidance from Senior Data Scientists. You will mentor junior colleagues and lead sub‑projects on larger initiatives.

• Data Management: Perform data availability/quality assessments, develop comprehensive ETL processes, execute data quality assessments, illustrate material understandings through coherent data visualizations, and conduct quality control.
• Data Analysis: Perform exploratory data analysis and render key insights through evidenced results, execute and tune statistical analyses.
• Modeling: Conduct necessary research for proof‑of‑concept model selection, identify viable model features and apply appropriate normalizations, appropriately log model parameters and metrics for comparison, diligently optimize current processes and pipelines.
• ModelOps: Actively work on ModelOps design and implementation.
• Engineering: Develop data pipelines and models that are well designed and auditable, develop production‑ready code that is scalable, maintainable, and adheres to best design principles, develop scalable Gen AI solutions.
• Communication: Frequently partner with internal and external stakeholders both technical and non‑technical, provide guidance to junior data scientists.

• Experience in ideating a proof‑of‑concept and independently managing workstreams in larger projects.
• Experience in mentoring junior resources.

• Bachelor's degree in Math, Physics, Computer Science, Statistics, or equivalent qualifications.
• A couple of years of data science experience.

• Proficiency in Python, including in depth knowledge of object‑oriented programming and design principles.
• Proficiency in SQL, including strong knowledge of database structure and efficiency.
• Proficiency in Git.
• Proficiency in any of the following AI/ML frameworks: Huggingface, Torch, TensorFlow, Scikit‑Learn, Spacy.
• Experience in any of the following additional platforms related to MLOps: MLFlow, dbt, Docker, Kubernetes.
• Experience with working cloud environments such as AWS.

• Experience in any of the following AI/ML topics within a work context: Natural Language Processing (NLP), forecasting, clustering, recommendation engines, network analysis (graph theory), regressions.
• Experience in any of the following AI/ML concepts: cross validation, ensemble methods, over/under fitting, accuracy/precision/recall, RAG, agents.
• Understanding of AI/ML performance measurement best practices including but not limited to how to build and apply a QA/QC framework and knowledge of drift, bias, and fairness.
• Experience in any of the following computer science concepts: hashing, space complexity, and parallel processing (multiprocessing).
• Ability to work with Linux/*nix systems, bash scripting, SSH.
• Experience in a Solution Architect role, with a demonstrated ability to design technical solutions including but not limited to Database Design, ModelOps, and Orchestration.