Technical Architect - Data Science

TESTQ Technologies Limited
Leicester
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Cloud Data Architect - Digital Transformation Leader

Flexible Senior Data Architect & Tech Lead

Data Architect

Integration & Data Architect

Data Architect

Data Architect

TQUKI0480_4937 - Technical Architect - Data Science

Job Type: Permanent


Work Mode: Remote


Job title: Technical Architect - Data Science


Job Purpose:


TESTQ Technologies is an IT services and Solutions Company whose offerings span over a variety of industry sectors with strong technical, domain, and processexpertisehelping clients grow their businesses and decrease operational costs on a continuous basis in an ever-changing business environment.


The Technical Architect – Data Science is responsible for designing, developing, and implementing end-to-end data and AI solutions. This role bridges data engineering, data science, and architecture by defining scalable frameworks, guiding model deployment, and ensuring optimal use of cloud and big data technologies.


Job Description (Main Duties and Responsibilities):



  • Design and architect for end-to-end data science and AI solutions aligned with enterprise strategy.
  • Define scalable data architectures for ingestion, processing, storage, and analytics.
  • Lead the design of machine learning pipelines, model deployment frameworks, and MLOps solutions.
  • Collaborate with data scientists, engineers, and analysts to operationalize ML models in production.
  • Evaluate and recommend tools, frameworks, and best practices for data science and AI initiatives.
  • Ensure compliance with data governance, security, and privacy standards.
  • Provide technical leadership and mentorship to the data science and engineering teams.
  • Optimize cloud and on-premises data architectures for performance, cost, and scalability.
  • Drive innovation through proof-of-concepts (POCs) and pilot implementations of emerging AI/ML technologies.

Key Skills, Qualifications and Experience Needed [The candidate must demonstrate these in all stages of assessment]



  • A bachelor's degree in computer science, Information Technology, or related discipline.
  • 3 to 4 years of professional experience in Technical Architect – Data Science roles.
  • Should have strong proficiency in programming and scripting languages such as Python, R, SQL, Java, Scala, and Shell scripting.
  • They should be adept at using data science and machine learning libraries including NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Keras, XGBoost, and LightGBM for building and deploying advanced analytical models.
  • A solid understanding of data engineering and big data ecosystems is essential, with hands-on experience using Apache Airflow, Luigi, and dbt for data workflow orchestration, and familiarity with Hadoop, Spark, Hive, Kafka, and Flink for distributed data processing.
  • Expertise in working with both relational and NoSQL databases such as PostgreSQL, MySQL, Oracle, MongoDB, Cassandra, and Redis is required, along with experience in managing data lakes and data warehouses like Snowflake, Databricks, Amazon Redshift, Google BigQuery, and Azure Synapse.
  • The architect should have deep experience with cloud platforms—including AWS (S3, Glue, SageMaker, EMR, Lambda), Microsoft Azure (Data Lake, Synapse, ML Studio, Databricks), and Google Cloud Platform (BigQuery, Vertex AI, Dataflow, AI Platform)—and the ability to design scalable, cloud-native data solutions.
  • Proficiency in MLOps and DevOps tools such as MLflow, Kubeflow, DVC, and TensorFlow Extended (TFX) is required to enable model lifecycle management.
  • Knowledge of CI/CD pipelines using tools like Jenkins, GitHub Actions, Azure DevOps, or CircleCI, and experience with containerization and orchestration through Docker, Kubernetes, and Helm, is highly desirable. Familiarity with model monitoring and governance tools such as Evidently AI, WhyLabs, and Neptune.ai will be advantageous.
  • The role also requires expertise in data visualization and business intelligence tools including Power BI, Tableau, Looker, Superset, Plotly, and Dash for translating analytical insights into actionable business intelligence.
  • Additionally, strong understanding of API design and integration (REST, GraphQL), version control systems (Git, GitLab), and data security and compliance frameworks such as GDPR and HIPAA is important.

Qualifications: Bachelor's degree or above in the UK or Equivalent.


Salary: GBP 55,000 to GBP 65,000 per annum


Published Date: 03 November 2025


Closing Date: 02 December 2025


Evaluation: CV Review, Technical Test, Personal and Technical Interview and References


Job Type: Full-time, Permanent [Part time and Fixed Term option is available]


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Jobs

How to Write a Data Science Job Ad That Attracts the Right People

Data science plays a critical role in how organisations across the UK make decisions, build products and gain competitive advantage. From forecasting and personalisation to risk modelling and experimentation, data scientists help translate data into insight and action. Yet many employers struggle to attract the right data science candidates. Job adverts often generate high volumes of applications, but few applicants have the mix of analytical skill, business understanding and communication ability the role actually requires. At the same time, experienced data scientists skip over adverts that feel vague, inflated or misaligned with real data science work. In most cases, the issue is not a lack of talent — it is the quality and clarity of the job advert. Data scientists are analytical, sceptical of hype and highly selective. A poorly written job ad signals unclear expectations and immature data practices. A well-written one signals credibility, focus and serious intent. This guide explains how to write a data science job ad that attracts the right people, improves applicant quality and positions your organisation as a strong data employer.

Education Jobs

Maths for Data Science Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are applying for data science jobs in the UK, the maths can feel like a moving target. Job descriptions say “strong statistical knowledge” or “solid ML fundamentals” but they rarely tell you which topics you will actually use day to day. Here’s the truth: most UK data science roles do not require advanced pure maths. What they do require is confidence with a tight set of practical topics that come up repeatedly in modelling, experimentation, forecasting, evaluation, stakeholder comms & decision-making. This guide focuses on the only maths most data scientists keep using: Statistics for decision making (confidence intervals, hypothesis tests, power, uncertainty) Probability for real-world data (base rates, noise, sampling, Bayesian intuition) Linear algebra essentials (vectors, matrices, projections, PCA intuition) Calculus & gradients (enough to understand optimisation & backprop) Optimisation & model evaluation (loss functions, cross-validation, metrics, thresholds) You’ll also get a 6-week plan, portfolio projects & a resources section you can follow without getting pulled into unnecessary theory.

Jobs

Neurodiversity in Data Science Careers: Turning Different Thinking into a Superpower

Data science is all about turning messy, real-world information into decisions, products & insights. It sits at the crossroads of maths, coding, business & communication – which means it needs people who see patterns, ask unusual questions & challenge assumptions. That makes data science a natural fit for many neurodivergent people, including those with ADHD, autism & dyslexia. If you’re neurodivergent & thinking about a data science career, you might have heard comments like “you’re too distracted for complex analysis”, “too literal for stakeholder work” or “too disorganised for large projects”. In reality, the same traits that can make traditional environments difficult often line up beautifully with data science work. This guide is written for data science job seekers in the UK. We’ll explore: What neurodiversity means in a data science context How ADHD, autism & dyslexia strengths map to common data science roles Practical workplace adjustments you can request under UK law How to talk about your neurodivergence in applications & interviews By the end, you’ll have a clearer sense of where you might thrive in data science – & how to turn “different thinking” into a real career advantage.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.