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.
We develop, adapt, tune, and scale advanced machine learning models, leveraging a high-performance cloud-based tech stack. Our team are encouraged to voice their thoughts on developing and improving any of our project pipelines. Our projects range from long-term to proof-of-concept in nature, in such diverse areas as litigation support, time-series analysis, geo-spatial modelling, and communications analytics. The spectrum of projects that you will be exposed to should provide you with a holistic impression of life as a data scientist.
Examples of current engagements include:
- Automated document classification using NLP
- Geospatial modelling of electric vehicle demand
- Communication/reputation analytics
- Identifying sanctions breaches with unsupervised learning
- Graph analysis of financial data
- Anomaly detection in API call data
What you will do:
As a Data Scientist with FTI Consulting, you'll work alongside people from a range of backgrounds. Within your first year, you will have the chance to work on multiple projects and proof-of-concepts in different domains, with opportunities to specialise according to what interests you. Your colleagues will support you, and help you to grow in both technical and business capabilities.
You'll develop advanced machine learning models, leveraging high performance servers within an AWS tech stack to test and deploy your work. With a strong focus on learning and R&D, and an environment that values input from employees of all levels, you'll be expected to put forward your own ideas, and encouraged to upskill in areas that interest you-particularly as we expand our capabilities to include distributed computing and GPU accelerated training.
Some of the day to day tasks include:
- Descriptive analysis of client datasets
- Enrich existing project datasets with new sources through research
- Statistical analysis, transformation, and cleaning of datasets
- Propose and implement (code) alternatives and refinements to models, to enhance pipeline robustness, efficiency, and performance
- Research into latest progress in the realm of machine learning and statistical analysis, and how these developments can be applied to current project pipelines or future prospects
What we offer you:
You will have the chance to work on multiple projects and proof-of-concepts in different domains, with opportunities to specialise.
Your colleagues will support you, and help you to grow in both technical and business capabilities.
You will develop, deploy, and scale your models, leveraging cloud computing within an AWS tech stack.
With a strong focus on learning and R&D, and an environment that values input from the entire team, you'll be expected to put forward your own ideas, and encouraged to upskill in areas that interest you - particularly as we expand our capabilities to include distributed computing and GPU accelerated training.
- A minimum of a 2:1 bachelor's degree in a subject that involves computer science, technology, data analytics, or any other STEM subjects
- Experience with Python or other programming language
- Experience with basic data wrangling/cleaning in a dataframe package (e.g. R's data.frame, or Python's pandas)
- Familiarity with an SQL-like language
- Reasonable knowledge of fundamental computer science concepts (time/space complexity, hashing, simple algos for sorting/searching/etc.)
- At least a basic foundational stats course (confidence regions, families of distributions, r.vs, measures of fit)
- Knowledge of general ML concepts (training/test, cross-validation, supervised/unsupervised learning, regression and classification, clustering, over/under-fitting, ensemble methods, dimension reduction and feature extraction)
- For at least a few specific models-e.g. trees, forests, FFNNs, gradient boosting, SVMs, k-means... though not necessarily all of these-to know how they actually work, algorithms involved, etc.
- Having worked with linux/*nix systems, bash scripting, SSH, etc. is useful but not expected
- Familiarity of ML frameworks like Torch or TensorFlow, or big data frameworks like Spark might be desirable, but definitely not essential
- Experience with Git and Docker is desirable
- Passion for problem-solving and strong analytical skills
- Ability to be a team player
- Willingness to research, analyse and develop new skills
- Excellent communication, interpersonal and organisational skills
Apart from the well-structured career path and excellent team environment, our employees enjoy a variety of perks and benefits. We offer a competitive benefits and wellbeing programme including:
- private medical insurance
- dental insurance
- life insurance
- income protection
- flex critical illness cover
- 5% employer pension contribution
- holiday buy
- discounted gym membership
- interest free travel loans
- paid volunteer hours
- corporate matching for charitable donations
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 our website and connect with us on Twitter, Facebook and LinkedIn.
FTI Consulting is an equal opportunity employer and does not discriminate on the basis of race, colour, 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.