Data Analyst

Our Graduates
London
2 weeks ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

Data Analyst

About the job
Company Information

All the health information we need is within us. Just below the skin. Sava is redefining the way people interact with their health by developing the most advanced biosensing technology science has to offer, capable of accessing bodily information in a painless, real-time and affordable way.

Description

As a Data Analyst in our Research and Development team, you will play an essential role in analyzing data from electrochemical lab tests, integrating insights from production and quality control systems, and supporting sensor development. Working closely with our R&D teams, your contributions will optimize analysis libraries, refine test result interpretations, and create impactful visualizations for internal decision-making.

Responsibilities

Utilize Python and SQL to manipulate, analyze, and extract insights from complex datasets.Perform statistical analyses and interpret test data to support sensor performance and R&D efforts.Collaborate with cross-functional teams to develop and optimize data analysis processes.Build and maintain dashboards and reports to communicate data insights to stakeholders.

Past Experience

1-2 years of experience in a similar role.

Requirements

Advanced proficiency in Python for data manipulation and analysis.Strong experience with SQL for querying relational databases.Knowledge of statistical analysis and core statistical concepts.Detail-oriented with strong problem-solving abilities and analytical skills.Excellent communication skills for collaboration across diverse teams.

Preferred

Familiarity with Git for version control.Experience with Power BI or other visualization tools.Understanding of data cleaning techniques and data quality management.Knowledge of signal processing or filtering techniques for lab data.

Tagged as: Analysis, IT, Research

#J-18808-Ljbffr

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.

Tips for Staying Inspired: How Data Science Pros Fuel Creativity and Innovation

Data science sits at the dynamic intersection of statistics, computer science, and domain expertise, driving powerful innovations in industries ranging from healthcare to finance, and from retail to robotics. Yet, the daily reality for many data scientists can be a far cry from starry-eyed talk of AI and machine learning transformations. Instead, it often involves endless data wrangling, model tuning, and scrutiny over metrics. Maintaining a sense of creativity in this environment can be an uphill battle. So, how do successful data scientists continue to dream big and innovate, even when dealing with the nitty-gritty of data pipelines, debugging code, or explaining results to stakeholders? Below, we outline ten practical strategies to help data analysts, machine learning engineers, and research scientists stay inspired and push their ideas further. Whether you’re just starting out or looking to reinvigorate a long-standing career, these pointers can help you find fresh sparks of motivation.

Top 10 Data Science Career Myths Debunked: Key Facts for Aspiring Professionals

Data science has become one of the most sought-after fields in the tech world, promising attractive salaries, cutting-edge projects, and the opportunity to shape decision-making in virtually every industry. From e-commerce recommendation engines to AI-powered medical diagnostics, data scientists are the force behind innovations that drive productivity and improve people’s lives. Yet, despite the demand and glamour often associated with this discipline, data science is also shrouded in misconceptions. Some believe you need a PhD in mathematics or statistics; others assume data science is exclusively about machine learning or coding. At DataScience-Jobs.co.uk, we’ve encountered a wide array of myths that can discourage talented individuals or mislead those exploring a data science career. This article aims to bust the top 10 data science career myths—providing clarity on what data scientists actually do and illuminating the true diversity and inclusiveness of this exciting field. Whether you’re a recent graduate, a professional looking to pivot, or simply curious about data science, read on to discover the reality behind the myths.

Global vs. Local: Comparing the UK Data Science Job Market to International Landscapes

How to evaluate salaries, opportunities, and work culture in data science across the UK, the US, Europe, and Asia Data science has proven to be more than a passing trend; it is now a foundational pillar of modern decision-making in virtually every industry—from healthcare and finance to retail and entertainment. As the volume of data grows exponentially, organisations urgently need professionals who can transform raw information into actionable insights. This high demand has sparked a wave of new opportunities for data scientists worldwide. In this article, we’ll compare the UK data science job market to those in the United States, Europe, and Asia. We’ll explore hiring trends, salary benchmarks, and cultural nuances to help you decide whether to focus your career locally or consider opportunities overseas or in fully remote roles. Whether you’re a fresh graduate looking for your first data science position, an experienced data professional pivoting from analytics, or a software engineer eager to break into machine learning, understanding the global data science landscape can be a game-changer. By the end of this overview, you’ll be better equipped to navigate the expanding world of data science—knowing which skills and certifications matter most, how salaries differ between regions, and what to expect from distinct work cultures. Let’s dive in.