Senior Director, Data Science

FTI Consulting

  • Full Time

Our data scientists employ advanced mathematics, statistics, and computer science (ML and algorithmic), together with industry knowledge, to tackle business problems for clients ranging from governments to Silicon Valley tech giants. We also serve as an R&D function for the company, developing internal tools and proof-of-concepts to put us ahead of the market.

The team is technologically agnostic and has software engineering skills to deploy advance ML/AI methodologies to scale.

The team works on complex high profile projects and apply methodologies to several industries and fields, from Financial Services to e-Mobility, from Reputation Management to Litigation Support. We develop and deploy advanced models as well as end-to-end Data Science solutions, leveraging a high-performance cloud-based tech stack.

We favour people who have a pragmatic approach to problem-solving and are capable of developing solutions that clients can rely on in production. You should be capable of challenging your own work, and  finding balance between perfection and practicality.
Crucially, the Senior Director is responsible for the technical progression of Data Scientists, and as a mentor must enjoy sharing knowledge as well as learning from others. Furthermore, they must be promoting best practices in developing and deploying ML solutions across the team and the broader firm.

The Senior Director must be able to lead and get along with people from a variety of backgrounds, to contribute to discussions about methodology, recognise the strengths of different colleagues, and demonstrate business acumen.
What You’ll Do

A Senior Director of Data Science directly performs or coordinates the following activities.

At the data preparation stage of the project:

  •  assessment of the data and, if needed, design of the process needed to correctly prepare data
    •    technical leadership and be responsible for the team
    •    data availability/quality assessment
    •    ETL processes: SQL queries or scripts to query APIs; research and acquisition of data from public sources; liaising with clients to ingest unstructured data; choosing appropriate storage schemas in conjunction with engineers/database architects
    •    identification of issues with the data—missing values, (near) duplicates, ambiguous name-entity resolution, non-standardised addresses, dates, etc.—and data cleaning
    •    preparation of reproducible processing scripts/notebooks in project repository, and meaningful documentation
    •    report results and flag potential blockers to the Managing Director of Data Science working with them to a mitigation strategy

At the proof of concept stage of the project:

  •  design the process needed to deliver the PoC
    •    supported by the Managing Director of Data Science assessment of time and resources needed to deliver the PoC
    •    responsible for the team working on the PoC acting as technical lead
    •    deliverable timeline management
    •    responsible for QA on PoC results and methodology applied
    •    sign off the methodology applied to deliver the PoC
    •    perform exploratory data analysis—how is data distributed and what hypotheses might be supported and visualisation of key findings
    •    identification of possible model features and applies appropriate transformations or normalisation
    •    design and develop a “quick and dirty” approach, likely using off-the-shelf libraries and a reduced sample of available data, to produce initial insights or demonstrate feasibility
    •    communication of outcomes to non-data scientists and actively feeds into discussion about what is achievable within project timelines, what might be other areas of opportunity
    •    supported by the Managing Director of Data Science manages internal and external stakeholders
    •    reports results and flag criticalities to the Managing Director of Data Science working with them to a mitigation strategy

At production stage of the project:

  •  is responsible for the team working on the deployment project
    •    is responsible for the connection between the Data Science team and any other team eventually involved in the project
    •    engineers variables into usable features
    design, validate and develops robust models and statistical analyses, employing appropriate measures of fit to validate their own and team’s work
    •    build and own complete pipelines, potentially working alongside engineers/developers, and testing these work at scale
    •    coordinate work and responsibilities to the team managing and balancing skills and level of expertise
    •    find and fix bugs in pipeline components (ideally before they go into production!)
    •    code optimisation for time/space efficiency, as needed
    •    responsibility for running new iterations of data (retraining model, or new inputs for prediction) through the existing pipeline as required, performs validation of these results and prepares client deliverables
    •    responsible for QA process and for the robustness of the solution delivered
    •    communication of findings to client, and incorporates their feedback / domain knowledge to tune model
    •    report results and flag issues to the Managing Director of Data Science in case working with them to a mitigation strategy

At platform development stage, the Director of Data Science:

  •  is responsible for the team working on the development project
    •    owns the design of the solution and is responsible for its development
    •    is responsible for the connection between the Data Science team and any other team eventually involved in the project
    •    exposes model functionality to development team—e.g. as a package, or an API
    •    supports engineers / developers / UI team in building end-to-end solution, advocates for needs of client and data science team
    •    reports results and flag issues to the Managing Director of Data Science working with them to a mitigation strategy

Qualifications & Experience

Educated to degree level within a technical discipline

  •  Highly experienced in a Data Science role at both proof-of-concept and production stages
    •    Highly experienced in a managing end-to-end data science projects
    •    Proficient in SQL-like language and Python
    •    Advanced understanding of computer science concepts (time/space complexity, hashing, simple algos for sorting/searching/etc.)
    •    Advanced understanding of general ML concepts (training/test, cross-validation, supervised/unsupervised learning, regression and classification, clustering, over/under-fitting, accuracy/precision/recall, ensemble methods, dimension reduction and feature extraction)
    •    Highly experienced in developing models of different topologies, including but not limited to forests, kernels, evolutionary algorithm, and neural networks
    •    Experience with backend tools like docker, mlflow, airflow, databases
    •    Knowledge of frontend solutions for serving models and deliverables
    •    Ability of explaining complex machine learning models to clients without technical backgrounds
    •    Ability to translate business problems into proposed solution architecture
    •    Demonstrated advanced expertise in at least one area—e.g. computer vision, NLP, geospatial modelling, operations research, financial investigations (this could be running projects, papers written, a research posting—we want to see what you can bring to the team to enhance existing propositions or enable new ones)
    •    Able to work with linux/*nix systems, shell scripting, containers, and AWS environments
    •    Experience developing modularised code, using version control with git—other DevOps knowledge, such as containerisation using Docker, highly desirable
    •    Familiarity with an ML framework like Torch or TensorFlow
    •    Familiarity with a distributed framework like Spark

Our Benefits 

Apart from the well-structured career path and excellent team environment, our employees enjoy a variety of perks and benefits. Our benefits include, but are not limited to:

  • Competitive salary and bonus plans
  • Generous paid holidays and paid time off
  • Annual paid volunteer hours
  • Corporate matching for charitable donations
  • Free snacks and drinks

About FTI Consulting

What makes us unique? With more than 6,250 employees located in offices in every corner of the globe, we are the firm our clients call when their most important issues are at stake. Regardless of what level you are, you will have the opportunity to work alongside and learn from top experts in your field on high-profile engagements that impact history. Our culture is collaborative, and we value diversity, recognition, development and making a difference in our communities.

FTI Consulting is publicly traded on the New York Stock Exchange and has been recognized as a Best Firm to Work For by Consulting magazine and one of America’s Best Management Consulting Firms by Forbes. For more information, visit and connect with us on Twitter (@FTIConsulting), Facebook and LinkedIn.

FTI Consulting is an equal opportunity employer and does not discriminate on the basis of race, color, national origin, ancestry, citizenship status, protected veteran status, religion, physical or mental disability, marital status, sex, sexual orientation, gender identity or expression, age, or any other basis protected by law, ordinance, or regulation.