Data Scientist - Hybrid

Overview

Join to apply for the Data Scientist - Hybrid role at TieTalent.

Responsibilities

• Deliver high-quality data science and analytics solutions, contributing to design, development, and product roadmaps.
• Collaborate with clients and internal teams to gather requirements, analyse data, and validate solutions.
• Develop and implement descriptive, predictive, and prescriptive analytics, integrating data from multiple sources.
• Produce clear documentation, reports, and visualisations.
• Provide technical input for proposals, solution scoping, and proofs-of-concept.
• Attend occasional client meetings or events across the UK, Europe, and internationally.

Required Experience

• Strong knowledge of data modelling, machine learning, and/or advanced data analytics.
• Demonstrable track record of delivering data analytics projects as part of a team.
• Hands-on experience with collaborative software development and version control (preferably Git).
• Familiarity with Agile/SCRUM methodologies.
• Exposure to pre-engagement activities such as project scoping, technical feasibility analysis, or prototype development.
• Comfortable contributing to technical discussions and implementing solutions defined by project leads.

Desirable Experience

• Strong Python expertise.
• Experience with GNU/Linux environments.
• Familiarity with key data science and ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow, XGBoost, Hugging Face).
• Experience in natural language processing, tabular data analysis, or computer vision.
• SQL proficiency.
• Exposure to containerisation (Docker, Kubernetes) and cloud-native architectures.
• Experience with CI/CD, automated testing, and iterative product development.
• Knowledge of graph databases and graph analysis.

Benefits

• 35 days annual leave (including public holidays) plus up to 10 days unpaid leave.
• Flexible working arrangements around core hours.
• Private health insurance and pension scheme.
• Contribution to gym membership.
• Ongoing professional development support (courses, certifications, conferences).
• Regular company outings, team celebrations, and knowledge-sharing sessions.
• Monthly recognition of outstanding performance.

Additional Information

ALL APPLICANTS MUST BE FREE TO WORK IN THE UK.

Exposed Solutions is acting as an employment agency for this client. The advertisement does not discriminate and we welcome applications from any qualified persons.

Seniority level

• Mid-Senior level

Employment type

• Full-time

Job function

• Engineering and Information Technology

Industries

• Technology, Information and Internet

• Strong knowledge of data modelling, machine learning, and/or advanced data analytics.
• Demonstrable track record of delivering data analytics projects as part of a team.
• Hands-on experience with collaborative software development and version control (preferably Git).
• Familiarity with Agile/SCRUM methodologies.
• Exposure to pre-engagement activities such as project scoping, technical feasibility analysis, or prototype development.
• Comfortable contributing to technical discussions and implementing solutions defined by project leads.

Desirable Experience

• Strong Python expertise.
• Experience with GNU/Linux environments.
• Familiarity with key data science and ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow, XGBoost, Hugging Face).
• Experience in natural language processing, tabular data analysis, or computer vision.
• SQL proficiency.
• Exposure to containerisation (Docker, Kubernetes) and cloud-native architectures.
• Experience with CI/CD, automated testing, and iterative product development.
• Knowledge of graph databases and graph analysis.

• 35 days annual leave (including public holidays) plus up to 10 days unpaid leave.
• Flexible working arrangements around core hours.
• Private health insurance and pension scheme.
• Contribution to gym membership.
• Ongoing professional development support (courses, certifications, conferences).
• Regular company outings, team celebrations, and knowledge-sharing sessions.
• Monthly recognition of outstanding performance.