Optimization, Analytics & Recruitment Solutions Data Analytics Undergraduate

Pfizer
Woking
2 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Architect

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

Lead Data Engineer

SAS Data Engineer

Pfizer UK Undergraduate Programme 2026/2027
Optimization, Analytics & Recruitment Solutions

Undergraduate – Data Analytics

Clinical Development and Operations, Optimization, Analytics & Recruitment Solutions

Who can apply?
Applicants

must

be completing a placement as part of a degree course at a UK University, either through Year in Industry/Industrial Placement or Gap Year.
Please note that we will only consider candidates who have applied by completing the Pfizer Placement Application Form. Candidates who do not complete and attach the application form will NOT be considered.

You can download the Word version of the Application Form here: Undergraduate Vacancies | Pfizer UK and find instructions as to how to complete your application and more about eligibility criteria.
To learn more about this exciting opportunity, please see below!!

Department Overview

The Optimization, Analytics & Recruitment Solutions (OARS) team provides fit for purpose, robust intelligence and data insights designed to optimize performance of the Research & Development (R&D) engine providing analytics & intelligence to customers and stakeholders across clinical and regulatory functions within Pfizer Research Development and Pfizer Oncology Development.

Within OARS, the team look at the goals and protocols of clinical studies to assess their feasibility and drive decision making through providing country- and site-specific analysis and assumptions. OARS partner with the clinical study team to optimize plans for country and site selection using accurate data analysis, data-driven estimates and scenario planning. Data aggregation and visualization tools will be used to comprehend quality signals for improved performance analytics and historical trend analyses.

In addition, the team provides the analytical support and development of predictive models that enable and support Pfizer’s portfolio of clinical trials.

What can I achieve and what will I be accountable for whilst completing a placement at Pfizer?

The successful candidate will have the opportunity to develop an in-depth comprehension of the key elements of the planning stages of clinical studies. Through working in OARS, you will get hands-on experience in many critical aspects of using data to support key decisions being taken within Research and Development.

All successful applicants will have their assignment in OARS - working closely with experienced members of the team. The successful applicant will become part of the OARS team and will be expected to develop a sound technical grasp of the key stages in country & site selection, clinical study protocol optimization, enrollment modelling and ensuring quality is at the heart of all we do.

What other opportunities and benefits do Pfizer offer?

OARS partners closely with other functions within Pfizer and as such the successful candidate has the opportunity to gain insights to other core clinical development functions including:
Clinical Development
Project Management
Sourcing Compliance Management

We aim to facilitate an awareness of other areas of the business across the course of the placement, to provide a rounded awareness of the pharmaceutical business.

Successful applicants will also have the opportunity to have access to an extensive library of training tools and participate in regular educational sessions.

Candidates will be offered participation in voluntary events such as STEM (Science, Technology, Engineering and Math) supported activities and are actively encouraged to get involved in delivering science demonstrations to school pupils as part of the Pfizer STEM outreach programme, at events like Science Fairs.

When can I start?

Placements will start on 1st September 2026 and will run for 12 months.

PERSON SPECIFICATION

Type of person we are looking for, in relation to ‘ Skills ’, ‘ Knowledge ’ and ‘ Motivation ’:
Completing placement as part of University Degree at a UK University either through Year In Industry/Industrial Placement or Gap Year
On target for a good Degree Classification
Self-motivated with ability to work freely
Demonstrated effectiveness working as part of a team
Strong verbal and written communication skills
Detail focused
Solution oriented and good joint problem-solving abilities
High IT literacy (experience in Word, Excel, PowerPoint)

Please note that we only accept application forms. Please do not send over your CV or cover letter as they will not be considered.
Who can apply?

Applicants must be completing placement as part of a degree course at a UK University, either through Year in Industry/Industrial Placement or Gap Year.

This position will close for applications on 4th January 2026.

Please note that we only accept application forms. Please do not send over your CV or cover letter as they will not be considered.

Please access the Word version of the Application Form here: Undergraduate Vacancies | Pfizer UK and find instructions as to how to complete your application and more about eligibility criteria.

#LI-PFE

Purpose
Breakthroughs that change patients' lives ... At Pfizer we are a patient centric company, guided by our four values: courage, joy, equity and excellence. Our breakthrough culture lends itself to our dedication to transforming millions of lives.

Digital Transformation Strategy
One bold way we are achieving our purpose is through our company wide digital transformation strategy. We are leading the way in adopting new data, modelling and automated solutions to further digitize and accelerate drug discovery and development with the aim of enhancing health outcomes and the patient experience.

Flexibility
We aim to create a trusting, flexible workplace culture which encourages employees to achieve work life harmony, attracts talent and enables everyone to be their best working self. Let’s start the conversation!

Equal Employment Opportunity
We believe that a diverse and inclusive workforce is crucial to building a successful business. As an employer, Pfizer is committed to celebrating this, in all its forms – allowing for us to be as diverse as the patients and communities we serve. Together, we continue to build a culture that encourages, supports and empowers our employees.

DisAbility Confident
We are proud to be a Disability Confident Employer and we encourage you to put your best self forward with the knowledge and trust that we will make any reasonable adjustments necessary to support your application and future career. Our mission is unleashing the power of our people, especially those with unique superpowers. Your journey with Pfizer starts here!

Support Services

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.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.