How to Get a Better Data Science Job After a Lay-Off or Redundancy

Contents

• Understanding Redundancy in Data Science

• Step 1: Reset and Reflect

• Step 2: Define Your Strengths and Specialisms

• Step 3: Update Your CV and Data Science Portfolio

• Step 4: Optimise LinkedIn and Kaggle/GitHub Profiles

• Step 5: Reach Out to Recruiters and Hiring Managers

• Step 6: Apply Strategically and Follow Up

• Step 7: Upskill and Deepen Your Knowledge

• Step 8: Consider Contract, Research or Remote Roles

• Step 9: Look After Your Finances and Wellbeing

• Bonus: Top UK Employers Hiring Data Scientists in 2025

• Final Thoughts: Redundancy Can Be a Reset

Understanding Redundancy in Data Science

Data science teams can face redundancy due to budget cuts, changing business priorities, or shifting AI strategies. It’s not about your talent—it’s often just the numbers.

The UK still has strong demand for skilled data scientists who can:

• Deliver insights using Python, SQL, R

• Build predictive models (classification, regression, clustering)

• Use cloud tools (AWS, GCP, Azure)

• Communicate results effectively to stakeholders

Step 1: Reset and Reflect

Use your redundancy as a moment to re-centre:

• Reflect on past successes and challenges

• Clarify what kind of work and culture you want

• Consider which sectors interest you most (e.g. health, climate, fintech, edtech)

This clarity helps you avoid jumping into just anything.

Step 2: Define Your Strengths and Specialisms

Pinpoint your technical and domain strengths:

• Are you strongest in modelling, analytics, NLP, computer vision, or experimentation?

• Do you prefer building end-to-end solutions or deep-diving into model development?

• What tools and platforms do you know? (e.g. Pandas, Scikit-learn, TensorFlow, PyTorch, SQL, Power BI, dbt)

Step 3: Update Your CV and Data Science Portfolio

Your CV should include:

• A summary that reflects your direction and value

• Key achievements with measurable impact (e.g. “Reduced churn by 18% using a random forest model”)

• Tools, frameworks, and data sources used

• A link to your portfolio site, GitHub, or Kaggle profile

Keep it targeted and highlight transferable skills.

Step 4: Optimise LinkedIn and Kaggle/GitHub Profiles

LinkedIn Tips:

• Headline: “Data Scientist | Python, SQL, ML | Open to Work”

• About section: Summarise experience, focus areas, values

• Add featured projects, visualisations, or talks

GitHub/Kaggle Tips:

• Organise repos with clear READMEs and Jupyter notebooks

• Include problem statements, EDA, model results, and business interpretation

• Document any competitions or case studies

Sample LinkedIn About Section:

Data Scientist | Machine Learning | Open to Work

Experienced data scientist with 4+ years in predictive modelling, analytics, and stakeholder communication. Recently made redundant due to business restructuring, I’m now looking for a role where I can drive data-driven decisions and improve customer outcomes.

Tech stack: Python, SQL, Scikit-learn, Tableau, GCP, Git, NumPy, dbt

Let’s connect if you’re hiring or working on data science problems with real-world impact.

Step 5: Reach Out to Recruiters and Hiring Managers

Many data roles are filled through networks before job boards. Be proactive.

Recruiter Message Example:

Subject: Data Scientist | Available Immediately | ML & Analytics

Hi [Recruiter’s Name],

I’m exploring new data science opportunities after a recent redundancy. I bring experience in predictive modelling, SQL analysis, and stakeholder engagement. I’ve attached my CV and GitHub link, and would love to hear about any relevant openings.

Best regards,[Your Name][LinkedIn][GitHub][CV attachment]

Hiring Manager Follow-Up Example:

Subject: Application – Data Scientist Role at [Company Name]

Dear [Hiring Manager],

I recently applied for the Data Scientist role and wanted to express my strong interest. I have hands-on experience building models that drive measurable impact, and I’m currently available following a redundancy.

Please find my CV attached. I’d welcome the opportunity to speak further.

Kind regards,[Your Name]

Step 6: Apply Strategically and Follow Up

• Focus on roles where you match 70%+ of the criteria

• Customise your CV for each application

• Use data keywords from job ads (important for ATS)

• Keep a tracker of jobs, contacts, and application stages

Follow up if you’ve had no response after 10 days.

Step 7: Upskill and Deepen Your Knowledge

Use your time to grow your capabilities:

• Take courses on platforms like Coursera, DataCamp, Udacity, or fast.ai

• Contribute to open-source or build a case study project

• Learn tools like dbt, Airflow, Power BI, Looker

• Join virtual meetups or data science Slack groups

Step 8: Consider Contract, Research or Remote Roles

Short-term and flexible work can bridge the gap:

• Look for freelance or contract roles on platforms like Turing, Toptal, or Upwork

• Explore research assistant roles at universities or think tanks

• Check remote-friendly positions via www.datascience-jobs.co.uk

Step 9: Look After Your Finances and Wellbeing

Redundancy is stressful. Don’t overlook your personal care:

• Apply for any redundancy pay, Universal Credit, or JSA

• Use MoneyHelper or StepChange for financial guidance

• Build a daily routine for job search, skill-building, rest

• Stay connected to others in the industry to avoid isolation

Bonus: Top UK Employers Hiring Data Scientists in 2025

1. NHS England (Data & Analytics)

2. Deliveroo

3. The Alan Turing Institute

4. Monzo Bank

5. Meta (London AI & Data Science)

6. ASOS

7. Babylon Health

8. Spotify (UK Data Team)

9. Boots

10. GSK

11. YouGov

12. Starling Bank

13. Ocado Technology

14. BT Group

15. Government Digital Service (GDS)

Browse live roles at www.datascience-jobs.co.uk

Final Thoughts: Redundancy Can Be a Reset

Redundancy is not the end of your career—it could be the beginning of your most purposeful work yet.

With the right strategy, mindset, and visibility, you can land a role that plays to your strengths and future goals.

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