Step 1: Define Role Clusters & Your Keyword Map

Titles vary wildly. Build clusters so your searches catch synonyms & adjacent roles.

Core Data Science (Predictive/Descriptive)

• Titles: Data Scientist, Senior Data Scientist, ML Data Scientist, Applied Scientist.

• Keywords: Python, R, SQL, scikit-learn, XGBoost/LightGBM, statsmodels, feature engineering, cross-validation, SHAP, model monitoring.

Deep Learning, NLP & Computer Vision

• Titles: ML Scientist, NLP Scientist/Engineer, Computer Vision Scientist, LLM Engineer (DS-leaning).

• Keywords: PyTorch/TensorFlow/JAX, Transformers, Hugging Face, tokenisation, RAG, embeddings, LoRA/PEFT, OpenCV, detection/segmentation, ASR/TTS.

Product & Decision Science

• Titles: Product Data Scientist, Decision Scientist, Growth/Monetisation DS, Behavioural Scientist.

• Keywords: experiment design, A/B testing, CUPED, uplift modelling, funnels, retention, causal inference, metrics frameworks, SQL, dashboards.

Causal Inference & Experimentation

• Titles: Causal Data Scientist, Experimentation Scientist.

• Keywords: DiD, IV, RDD, propensity scores, matching, synthetic control, Bayesian A/B, DoWhy, EconML, CausalML.

Time Series & Forecasting

• Titles: Forecasting Data Scientist, Quant DS.

• Keywords: ARIMA/SARIMA, Prophet, TBATS, ETS, state-space, Kalman, LSTM/Temporal Fusion Transformer, seasonality, hierarchical reconciliation.

Recommenders & Personalisation

• Titles: Recommender Systems DS, Personalisation Scientist.

• Keywords: factorisation, implicit MF, sequence models, bandits, ranking metrics (MAP/NDCG), features at scale.

MLOps-adjacent (DS in production)

• Titles: ML Engineer (DS-leaning), ML Ops Data Scientist.

• Keywords: MLflow/W&B, model registry, feature stores, batch & real-time scoring, FastAPI, Docker/Kubernetes, CI/CD, monitoring & drift.

Analytics Engineering overlap

• Titles: Analytics Engineer (DS), DS-BI hybrid.

• Keywords: dbt, semantic layer, metrics layer, Looker/Power BI/Tableau, data contracts.

UK Locations & Modes

• London, Manchester, Bristol, Cambridge, Oxford, Edinburgh, Glasgow, Leeds, Birmingham, Newcastle, Reading, Belfast.

• Modes: Remote UK, Hybrid, On-site.

• Modifiers: “Visa sponsorship”, “Skilled Worker visa”, “Security clearance”, “Graduate”, “Internship”, “Permanent”.

Capture this keyword map—you’ll reuse it across alerts, feeds & prompts.

Step 2: Build Precise Boolean Searches (Copy & Paste)

Use these strings in Google & on job boards. Start broad, then refine with site: filters where signal lives.

General UK Data Science Search

("Data Scientist" OR "Applied Scientist" OR "Product Data Scientist" OR "Decision Scientist")
(Python OR "scikit-learn" OR "XGBoost" OR "LightGBM" OR PyTorch OR TensorFlow OR SQL)
(UK OR "United Kingdom" OR London OR Manchester OR Bristol OR Edinburgh OR Cambridge OR Leeds)
("Permanent" OR "Full-time") -site:indeed.co.uk -site:glassdoor.co.uk

Product/Decision Science (Experiments)

("Product Data Scientist" OR "Decision Scientist" OR "Experimentation Scientist")
("A/B testing" OR "experiment design" OR "CUPED" OR "causal" OR "uplift" OR "Bayesian")
(SQL OR Python) (UK OR "Remote UK")

NLP/LLMs

("NLP" OR "LLM" OR "Generative AI" OR "Applied Scientist")
(Transformers OR "Hugging Face" OR embeddings OR RAG OR "prompt tuning" OR LoRA)
(PyTorch OR TensorFlow) (UK OR "Remote UK")

Computer Vision

("Computer Vision" OR "CV Scientist" OR "Perception")
(PyTorch OR TensorFlow OR "object detection" OR segmentation OR "OpenCV")
(UK OR "Remote UK")

Time Series/Forecasting

("Forecasting" OR "Time Series" OR "Demand Planning")
(ARIMA OR Prophet OR "state space" OR "Kalman" OR LSTM OR TFT)
(UK OR "Remote UK")

Causal Inference

("Causal" OR "Econometrics" OR "Causal Inference" OR "Uplift")
(DiD OR "propensity score" OR "instrumental variables" OR RDD OR EconML OR DoWhy)
(UK OR "Remote UK")

MLOps-adjacent

("Machine Learning Engineer" OR "ML Engineer" OR "ML Ops" OR "MLOps")
(MLflow OR "model registry" OR "feature store" OR Docker OR Kubernetes OR "real-time scoring")
(UK OR "Remote UK")

Graduate & Early Career

("Graduate" OR "Junior" OR "Internship") ("data scientist" OR "applied scientist") (Python OR R OR SQL) (UK OR "Remote UK")

Visa/clearance (optional)

("Data Scientist" OR "Applied Scientist") ("visa sponsorship" OR "Skilled Worker visa" OR "security clearance") (UK)

ATS & Employer Career Sites (cut aggregator noise)

("Data Scientist" OR "Applied Scientist" OR "Product Data Scientist" OR "Decision Scientist")
(site:boards.greenhouse.io OR site:lever.co OR site:workable.com OR site:ashbyhq.com OR site:smartrecruiters.com OR site:icims.com OR site:successfactors.com)
(UK OR "Remote UK")

Step 3: Turn Searches Into Google Alerts & RSS

Let roles come to you.

Setup (quick):

1. Open Google Alerts.

2. Paste a Boolean string.

3. Show options → choose At most once a day (daily is ideal) or As-it-happens if you’re sprinting.

4. Deliver to: select RSS feed (paste into Feedly/Inoreader) or email.

5. Create separate alerts per cluster: Product/Decision, NLP/LLM, Vision, Forecasting, Causal, MLOps-adjacent, Graduate.

Good alert examples (copy-paste):

("Product Data Scientist" OR "Decision Scientist") ("A/B testing" OR "experiment design" OR causal OR CUPED) (SQL OR Python) (UK OR "Remote UK")

("NLP" OR "LLM" OR "Applied Scientist") (Transformers OR "Hugging Face" OR embeddings OR RAG OR LoRA) (PyTorch OR TensorFlow) (UK OR "Remote UK")

("Data Scientist") (forecasting OR "time series" OR ARIMA OR Prophet OR LSTM) (UK OR "Remote UK")

("Machine Learning Engineer" OR "MLOps") (MLflow OR "model registry" OR "feature store" OR Docker OR Kubernetes) (UK OR "Remote UK")

Pro tips

• Keep one alert per intent for relevance.

• Pair locations sensibly: London + “Remote UK” catches most UK roles.

• Use -site: to mute noisy domains that overwhelm your feed.

Prefer RSS? Tag/star items & export starred roles as CSV—perfect for a weekly planning pass in ChatGPT.

Step 4: Use ChatGPT as Your “Data Science Job Scout”

Alerts & RSS give you raw listings. ChatGPT turns them into a shortlist with actions so you apply faster & better.

Reusable system prompt (edit to your targets):

System role: You are my Data Science Job Scout for UK roles. Parse pasted job listings (title, company, location, link, snippet), remove duplicates by company+title+location, and produce a ranked shortlist that matches my criteria. Then provide tailored actions for each role.

My criteria:
• Target clusters: Product/Decision Science, Core DS (ML), NLP/LLM, CV, Forecasting, Causal, MLOps-adjacent.
• Must-haves (choose per cluster):
  - Product/Decision: experiment design, metrics, SQL, causal reasoning.
  - Core DS: Python, scikit-learn, feature engineering, evaluation, SQL.
  - NLP/LLM: Transformers/HF, embeddings/RAG, prompt/LoRA, PyTorch/TensorFlow.
  - CV: detection/segmentation, PyTorch, OpenCV, evaluation metrics.
  - Forecasting: ARIMA/Prophet/state-space/LSTM/TFT, hierarchy, accuracy metrics (MAPE/wMAPE/MASE).
  - Causal: DiD/IV/RDD/PSM, EconML/DoWhy, assumptions & diagnostics.
  - MLOps: MLflow/W&B, feature store, model registry, Docker/K8s, monitoring/drift.
• Location: Remote UK or London/Manchester/Cambridge/Bristol hybrid.
• Exclude: pure BI developer roles, contract <3 months, agency spam.

Output:
1) Summary: counts & duplicates removed; scoring logic (2 lines).
2) Ranked Shortlist (max 10): Title — Company — Location — Link — Score (0–100) — 1–2 line fit rationale.
3) Per-role actions:
   - 3 tailored CV bullets (impact-led, methods & outcomes).
   - 6–10 keywords to mirror (algorithms, tools, metrics).
   - A 3-sentence message to the hiring contact referencing one concrete requirement.
4) Today plan: order to apply with time estimates.

Daily run (paste your feed)

Here are today’s roles (Title — Company — Location — Link — Snippet):
1) ...
2) ...
Apply the Data Science Job Scout system prompt.

Deep-dive on a single role (for the perfect match)

Analyse this spec for must-haves, repeated terms & implied priorities. Then:
• Write 3 CV bullets that mirror the spec (cluster-appropriate), each ending with a measurable outcome.
• Draft a 120-word cover note referencing the product/domain & one 30-day quick win.
• List 10 keywords/phrases to include naturally (methods, tools, metrics).
• Provide 6 likely interview questions with succinct model answers using my background.

Job spec: [paste]
My background: [4–8 bullets with stack & outcomes]

Fast CV tailoring prompts (cluster-specific)

Product/Decision Science

Create 5 “Recent Impact” bullets covering experiment design, metric design, SQL analyses & uplift estimation—each with a metric (retention/ARPU/conv%). One line each, UK spelling.
Spec: [paste]

Core DS (ML)

Produce 5 bullets showing feature engineering, model selection, cross-validation & SHAP-driven insight—each with accuracy or business impact metrics.
Spec: [paste]

NLP/LLM

Write 5 bullets demonstrating Transformers/HF pipelines, embeddings/RAG & prompt/LoRA tuning—each with latency, quality or cost improvements.
Spec: [paste]

Computer Vision

Draft 5 bullets on detection/segmentation, data augmentation, evaluation (mAP/IoU), & deployment—each with measurable gains.
Spec: [paste]

Forecasting

Output 5 bullets on ARIMA/Prophet/LSTM/TFT, hierarchical reconciliation & accuracy gains (MAPE/MASE/wMAPE). One line each.
Spec: [paste]

Causal Inference

Provide 5 bullets on DiD/IV/RDD/PSM with assumption checks & sensitivity—each ending in a business decision influenced.
Spec: [paste]

MLOps-adjacent

Write 5 bullets on MLflow/W&B, feature store, registry, Docker/K8s & monitoring/drift—each with reliability/cost metrics.
Spec: [paste]

Step 5: Optional No-Code Automation (Email, Slack, Notion)

• Email filters: Route alert emails into a “DS-Jobs” label. Each morning, paste the best items into ChatGPT & run your Job Scout prompt.

• RSS rules: Tag feeds by cluster (Product/Decision, NLP, CV, Forecasting, Causal, MLOps). Star the best & export weekly as CSV for planning in ChatGPT.

• Notion/Sheets: Keep one tracker & paste it into ChatGPT for daily prioritisation & follow-up drafting.

• Slack/Discord: Pipe starred roles into a private channel via webhook for quick triage.

Step 6: A Simple Pipeline Tracker That Wins Interviews

Suggested columns

• Date found

• Role

• Company

• Location

• Link

• Cluster (Product/Core/NLP/CV/Forecasting/Causal/MLOps)

• Match score (0–100)

• Status (To apply / Applied / Interview / Offer / On hold / Rejected)

• Deadline / due date

• Contact (name, LinkedIn/email)

• Notes (methods, tools, metrics)

• Next action (what & when)

Follow-up rhythm

• T+3 days: polite nudge if no acknowledgement.

• T+10 days: request an update; include a small proof point (e.g., a brief redacted analysis or experiment plan—no confidential data).

• Post-interview: thank-you within 24 hours; reference one spec requirement & a 30-day quick win.

Shareable Prompt Library (Data Science-Specific)

1) Role Decoder

Explain this data science role in plain English: first 90-day deliverables, 3 hardest problems & the exact skills they truly need (methods, tools, metrics). Then list the top 12 CV keywords they’ll search for. [paste spec]

2) Company Fit Snapshot

From the spec & site notes, infer product/domain, data maturity (collection → modelling → deployment), experiment culture & MLOps posture. Output a 6-bullet “Why me, why now” pitch.
[spec + brief company notes]

3) CV Bullet Rewriter (Impact-led)

Rewrite these bullets with action+method+metric, mirroring the spec vocabulary (e.g., uplift, AUROC, MAPE, SHAP, CUPED, MLflow). One line each, UK spelling.
[bullets + spec]

4) Outreach Message (120 words)

Draft a concise message for the hiring contact that references one domain/stack detail (e.g., HF Transformers + RAG, or experiment platform) & proposes a 30-day quick win. Mirror 3 spec keywords. Confident tone, no fluff.
[spec + company notes]

5) Interview Pack Generator

Produce 8 technical questions + short model answers tailored to this spec (cluster-appropriate), plus 5 behavioural questions with STAR hints using my background.
[spec + background]

6) Offer & Salary Prep (UK)

Given the role, my years of experience & market norms, suggest a negotiation range in GBP, non-salary levers (training budget, conferences, compute credits, equity), & 3 crisp value statements I can use.
[spec + experience]

Keyword & Query Bank (Use Across Alerts, Feeds & Boards)

Titles
Data Scientist, Senior Data Scientist, Applied Scientist, Product Data Scientist, Decision Scientist, NLP Scientist, Computer Vision Scientist, Forecasting Data Scientist, Causal Data Scientist, ML Engineer (DS-leaning), Analytics Engineer (DS).

Methods & Algorithms
Regression/classification, gradient boosting (XGBoost/LightGBM/CatBoost), clustering, embeddings, Transformers, RAG, LoRA/PEFT, CNN/ViT, sequence models, bandits, Bayesian methods, causal (DiD/IV/RDD/PSM), time series (ARIMA/Prophet/LSTM/TFT), recommenders/ranking.

Tools & Platforms
Python, R, SQL, scikit-learn, PyTorch, TensorFlow, JAX, Hugging Face, Pandas/Polars, Statsmodels, MLflow/W&B, DVC, dbt, Airflow/Prefect/Dagster, Snowflake/BigQuery/Redshift/Databricks, Docker/Kubernetes, FastAPI.

Evaluation & Metrics
AUROC/AUPRC, logloss, accuracy/F1, MAP/NDCG, MAPE/MASE/wMAPE, uplift/ATE/CATE, latency, coverage, cost.

Analytics & Visualisation
Looker, Power BI, Tableau, matplotlib, seaborn, Plotly, experimentation platforms.

Governance & Risk
Model monitoring, drift, fairness/bias checks, differential privacy, GDPR/PII, approvals, model cards.

Modifiers
Remote UK, Hybrid, On-site, Permanent, Contract, Graduate, Internship, Visa sponsorship, Security clearance.

Sample Daily Workflow (7–12 Minutes)

1. Open your alert folder/RSS. Skim titles; bin obvious mismatches.

2. Paste 10–30 items into ChatGPT with your Data Science Job Scout prompt.

3. Review the shortlist. Open the top 3–5 high-score roles.

4. Run the deep-dive prompt on your favourite; generate tailored CV bullets & a 120-word cover note.

5. Update your tracker & set deadlines.

6. Apply in one sitting—mirror 6–10 keywords naturally (methods, tools, metrics).

7. Schedule follow-ups right away.

Consistency beats weekend blitzes.

Troubleshooting & Tuning

“Still getting noise.”
Tighten with stack tokens (Transformers, RAG, CUPED, ARIMA, MLflow) & exclude agency spam via -site: or -"recruitment agency".

“All senior roles.”
Include (Junior OR Associate OR "1–3 years") & exclude (Senior OR Principal OR Lead).

“Remote isn’t really UK-based.”
Use ("Remote UK" OR "UK-based remote" OR "right to work in the UK") & exclude "anywhere" if needed.

“I want product-heavy roles.”
Bias toward experiment & metric terms, add ("Product Data Scientist" OR "Decision Scientist"), exclude "research only" if it clutters results.

“I want research-heavy roles.”
Add ("Applied Scientist" OR "Research Scientist") + conferences (NeurIPS/ICLR/ICML/ACL/CVPR/EMNLP) & open-source signals.

Lightweight Tracker Template (Copy Text)

Date Found | Role | Company | Location | Link | Cluster | Match (0–100) | Status | Deadline | Contact | Notes | Next Action

Status: To apply / Applied / Interview / Offer / On hold / Rejected

Daily command for ChatGPT:
“From my tracker (below), propose today’s top 5 applications, fill missing ‘Next Action’, & draft follow-ups where Status=Applied & T+3 days.”

Copy-Paste Pack (Everything In One Place)

1) Google Alerts seeds

("Product Data Scientist" OR "Decision Scientist") ("A/B testing" OR CUPED OR causal OR uplift) (SQL OR Python) (UK OR "Remote UK")
("Data Scientist" OR "Applied Scientist") (Transformers OR "Hugging Face" OR RAG OR embeddings OR LoRA OR PyTorch OR TensorFlow) (UK OR "Remote UK")
("Data Scientist") (forecasting OR "time series" OR ARIMA OR Prophet OR LSTM OR TFT) (UK OR "Remote UK")
("Causal" OR "Econometrics" OR "Causal Inference" OR "Experimentation Scientist") (DiD OR IV OR RDD OR PSM OR EconML OR DoWhy) (UK OR "Remote UK")
("Machine Learning Engineer" OR "MLOps") (MLflow OR "model registry" OR "feature store" OR Docker OR Kubernetes) (UK OR "Remote UK")
("Graduate" OR "Junior" OR "Internship") ("data scientist" OR "applied scientist") (Python OR R OR SQL) (UK OR "Remote UK")

2) ATS-focused Google search

("Data Scientist" OR "Applied Scientist" OR "Product Data Scientist" OR "Decision Scientist")
(site:boards.greenhouse.io OR site:lever.co OR site:workable.com OR site:ashbyhq.com OR site:smartrecruiters.com OR site:icims.com OR site:successfactors.com)
(UK OR "Remote UK")

3) Data Science Job Scout (short version)

You are my UK Data Science Job Scout. From pasted listings, remove duplicates, rank by fit to my criteria, and output:
• Summary (counts + scoring)
• Top 10 roles (Title — Company — Location — Link — Score — 1-line why)
• Per-role actions (3 CV bullets, 6–10 keywords, 3-sentence outreach)
Criteria: [paste your clusters & must-haves]

4) Deep-dive tailoring

Analyse this spec. Return: 3 tailored CV bullets (action+method+impact), 10 keywords, a 120-word cover note referencing the product/domain & a 30-day quick win, & 6 interview Qs with model answers.
Spec: [paste]  |  Background: [paste]

5) Follow-up message

Please draft a concise follow-up for my application submitted on [date], referencing [one method/tool/metric] from the spec & reaffirming my fit in 2 sentences.

Final Thoughts

Your advantage isn’t endless scrolling—it’s repeatable, high-quality execution. Put discovery on autopilot with alerts & RSS, let ChatGPT act as your Data Science Job Scout, & ship one excellent application each day. Mirror the methods, tools & metrics the spec cares about, quantify impact, keep tight feedback loops—& you’ll move from scanning feeds to scheduling interviews, fast.

If you want a quick win, start with this alert:

("Product Data Scientist" OR "Decision Scientist") ("A/B testing" OR CUPED OR causal) (SQL OR Python) (UK OR "Remote UK")

Paste the first batch into ChatGPT with your Job Scout prompt—& enjoy your first hour back this week.