Technical Data Analyst

HCLTech
London
1 week ago
Applications closed

Related Jobs

View all jobs

Data Analyst, Power BI | Investment Management - Trading Platform | London, Hybrid | up to £100k ...

Data Analyst

Information and Data Governance Lead

Graduate Data Analyst

Graduate Data Analyst

Graduate Data Analyst

HCLTech is a global technology company, home to more than 220,000 people across 60 countries, delivering industry-leading capabilities centered around digital, engineering, cloud and AI, powered by a broad portfolio of technology services and products. We work with clients across all major verticals, providing industry solutions for Financial Services, Manufacturing, Life Sciences and Healthcare, Technology and Services, Telecom and Media, Retail and CPG, and Public Services. Consolidated revenues as of 12 months ending December 2024 totaled $13.8 billion.



We're looking for an experienced Technical Data Analyst with 10+ years of experience in data analysis, statistical modeling, and data visualization. The ideal candidate will have a strong background in data analysis, including data mining, predictive analytics, and data visualization. The Technical Data Analyst will be responsible for analyzing and interpreting complex data sets, developing statistical models, and creating data visualizations to inform business decisions.


Key Responsibilities:

1. *Data Analysis*: Analyze and interpret complex data sets, including data mining, predictive analytics, and data visualization.

2. *Statistical Modeling*: Develop and maintain statistical models, including regression analysis, time series analysis, and machine learning algorithms.

3. *Data Visualization*: Create data visualizations, including reports, dashboards, and interactive visualizations.

4. *STTM*: Develop and maintain STTM solutions, including data integration, data quality, and data governance.

5. *Collaboration*: Collaborate with cross-functional teams, including business stakeholders, data scientists, and IT teams, to ensure effective delivery of data solutions.

6. *Technical Leadership*: Provide technical leadership and guidance to junior team members, including mentoring and coaching.


Requirements:

1. *Experience*: 10+ years of experience in data analysis, statistical modeling, and data visualization.

2. *Data Analysis Knowledge*: Strong understanding of data analysis, including data mining, predictive analytics, and data visualization.

3. *Statistical Modeling Knowledge*: Strong understanding of statistical modeling, including regression analysis, time series analysis, and machine learning algorithms.

4. *Data Visualization Knowledge*: Strong understanding of data visualization, including reports, dashboards, and interactive visualizations.

5. *Programming Skills*: Proficiency in programming languages, such as Python, R, or SQL.

6. *Communication*: Excellent communication skills, with the ability to communicate technical concepts to non-technical stakeholders.


Nice to Have:

1. *Certifications*: Certifications in data analysis, statistical modeling, or data visualization, such as Certified Data Analyst or Certified Analytics Professional.

2. *Cloud Experience*: Experience with cloud-based data solutions, including AWS, Azure, or Google Cloud.

3. *DevOps*: Experience with DevOps practices, such as continuous integration and continuous deployment.

4. *Agile Methodologies*: Experience with Agile methodologies, such as Scrum or Kanban.

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Quantum-Enhanced AI in Data Science: Embracing the Next Frontier

Data science has undergone a staggering transformation in the past decade, evolving from a niche academic discipline into a linchpin of modern industry. Across every sector—finance, healthcare, retail, manufacturing—data scientists have become indispensable, leveraging statistical methods and machine learning to turn raw information into actionable insights. Yet as datasets grow ever larger and machine learning models become more computationally expensive, there are genuine questions about how far current methods can be pushed. Enter quantum computing, a nascent but promising technology grounded in the counterintuitive principles of quantum mechanics. Often dismissed just a few years ago as purely experimental, quantum computing is quickly gaining traction as prototypes evolve into cloud-accessible machines. When paired with artificial intelligence—particularly in the realm of data science—the results could be game-changing. From faster model training and complex optimisation to entirely new forms of data analysis, quantum-enhanced AI stands poised to disrupt established practices and create new opportunities. In this article, we will: Explore how data science has reached its current limits in certain areas, and why classical hardware might no longer suffice. Provide an accessible overview of quantum computing concepts and how they differ from classical systems. Examine the potential of quantum-enhanced AI to solve key data science challenges, from data wrangling to advanced machine learning. Highlight real-world applications, emerging job roles, and the skills you need to thrive in this new landscape. Offer actionable steps for data professionals eager to stay ahead of the curve in a rapidly evolving field. Whether you’re a practising data scientist, a student weighing up your future specialisations, or an executive curious about the next technological leap, read on. The quantum era may be closer than you think, and it promises to radically transform the very fabric of data science.

Data Science Jobs at Newly Funded UK Start-ups: Q3 2025 Investment Tracker

Data science has become an indispensable cornerstone of modern business, driving decisions across finance, healthcare, e-commerce, manufacturing, and beyond. As organisations scramble to capitalise on the insights their data can offer, data scientists and machine learning (ML) experts find themselves in ever-higher demand. In the UK, which has cultivated a robust ecosystem of tech innovation and academic excellence, data-driven start-ups continue to blossom—fuelled by venture capital, government grants, and a vibrant talent pool. In this Q3 2025 Investment Tracker, we delve into the newly funded UK start-ups making waves in data science. Beyond celebrating their funding milestones, we’ll explore the job opportunities these investments have created for aspiring and seasoned data scientists alike. Whether you’re interested in advanced analytics, NLP (Natural Language Processing), computer vision, or MLOps, these start-ups might just offer the career leap you’ve been waiting for.

Portfolio Projects That Get You Hired for Data Science Jobs (With Real GitHub Examples)

Data science is at the forefront of innovation, enabling organisations to turn vast amounts of data into actionable insights. Whether it’s building predictive models, performing exploratory analyses, or designing end-to-end machine learning solutions, data scientists are in high demand across every sector. But how can you stand out in a crowded job market? Alongside a solid CV, a well-curated data science portfolio often makes the difference between getting an interview and getting overlooked. In this comprehensive guide, we’ll explore: Why a data science portfolio is essential for job seekers. Selecting projects that align with your target data science roles. Real GitHub examples showcasing best practices. Actionable project ideas you can build right now. Best ways to present your projects and ensure recruiters can find them easily. By the end, you’ll be equipped to craft a compelling portfolio that proves your skills in a tangible way. And when you’re ready for your next career move, remember to upload your CV on DataScience-Jobs.co.uk so that your newly showcased work can be discovered by employers looking for exactly what you have to offer.