Senior Data Scientist
Listed on 2026-06-26
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IT/Tech
Machine Learning/ ML Engineer, Data Engineering, AI Engineer (Applied/Software), Data Analyst
The Client provides expert advisory and implementation services for open source big data solutions. As the first and only pure‑play big data services firm, their Data Scientists and Engineers are trusted advisors to the world's most innovative companies. Their experienced teams combine a distinctive methodology and a proven framework that includes tested design patterns and pre‑built components, to help clients build applications faster.
The Client helps customers leverage Big Data analytics by integrating open source platforms, such as Hadoop, No
SQL and Streaming Engines, with best‑of‑breed data warehousing environments. Service offers include: a Big Data roadmap, Data Engineering, Data Lake and Analytic Operations, Training and ongoing Big Data Solution Support.
The Clients Data Science team delivers insights and value to clients from heterogeneous data sets with solutions that integrate into engineering and decision‑making processes. Additionally, their team enables big data analytics for their clients through advisory services including use case prioritizations, tool selection and training, and capability definitions. Their success as a services firm relies on their experts' ability to be more than technologists and statisticians.
The Senior Data Scientist will be responsible for utilising advanced statistical and machine learning methods to answer business questions and deliverinsightful solutions to complex problems. The ideal candidate has excellent interpersonal and communication skills and can interact with business and technology stakeholders where necessary.
Specific Responsibilities- Design hypothesis tests, oversee test execution, and evaluate the results
- Model, predict and classify data
- Utilise machine learning and large‑scale data mining techniques to discover and identify actionable patterns in the data
- Help define and document business requirements and acceptance criteria
- Assist in or lead workshops and documenting relevant outcomes
- Identify opportunities and appropriate solutions (e.g. algorithms and libraries)
- Present to both technical and non‑technical stakeholders, internally and in a Customer facing capacity
- Contribute to sprint planning, provide realistic estimates and plan deliverables
- Attend stand‑ups and retrospectives
- Research, design, evaluate, build, tune and document end‑to‑end data science solutions
- Understand and solve scalability and production issues
- Adhere to coding standards and best practices
- Ensure all models are validated and all business logic is robustly tested
- Have a minimum of five years professional experience
- Have clear written and spoken English
- Have experience presenting to business stakeholders
- Have experience formalising data science solutions based on a set of business requirements
- Have demonstrable experience of delivering a data science solution to production
- Have knowledge and experience of the products software lifecycle
- Have an excellent understanding of machine learning and statistics
- Have well‑developed quantitative skills and analytical thinking
- Have experience in scaling data science methods and accounting for non‑functional requirements
- Have demonstrable professional experience in Python or R
- Have demonstrable professional experience in Scala, Java or C
- Be proficient with version control tools and strategies, ideally Git and Gitflow
- Have hands‑on work experience with Data analysis and visualisation tools and workbenches
- Have hands‑on work experience with Analysing structured, semi‑structured and unstructured data
- Have hands‑on work experience with Dataquery languages, e.g. SQL, HiveQL or similar
- Have experience in working in cross‑functional agile software engineering teams
- Have experience in reliably estimating, planning and meeting deadlines for deliverables
- Have experience mentoring Junior Data Scientists
- Hands‑on work experience or proficiency with Spark, Scikit‑learn and Pandas, Distributed systems, Hadoop ecosystem, Cloud‑based machine‑learning APIs
- Have sufficient knowledge of NLP
- Have experience with TDD, BDD and Continuous Integration
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