Pricing
Step 1: Clarify your specialisation. ML Engineer, Data Scientist, NLP, Computer Vision, Analytics Engineer, AI Researcher.
Step 2: Add your domain expertise (fintech, healthcare, e-commerce, logistics) and 2-3 core tools.
Step 3: Connect to a business problem you've solved or a measurable impact you've created.
"ML Engineer | NLP & LLMs | Shipping AI products in production, not just notebooks"
'Not just notebooks' is a pointed, knowing jab at the gap between research and deployment, resonates with hiring managers everywhere.
"Senior Data Scientist | Recommendation systems & personalisation | Built models serving 50M+ users"
Scale (50M+ users) is the most impressive signal for consumer tech DS roles.
"Data Scientist → Business Impact | Turning messy data into decisions that move revenue"
The arrow format signals a philosophy, this person bridges the gap between technical and business.
"Analytics Engineer | dbt, Snowflake, Looker | Building data infrastructure that actually gets used"
'That actually gets used' is a sharp critique of unused data work, very relatable for data leaders.
"Healthcare Data Scientist | Clinical NLP & predictive modelling | HIPAA compliant pipelines | ex-NHS"
Domain (healthcare) + compliance signal (HIPAA) + credibility (ex-NHS) = rare combination in a specialized field.
"Fintech Data Scientist | Fraud detection & credit risk | Python + Spark | Reduced fraud losses by $12M"
The dollar metric ($12M) is extremely rare in DS headlines, makes this profile impossible to forget.
"AI Researcher | Computer Vision & Generative Models | 8 papers, 1200+ citations | Open to industry roles"
Publication count + citation count is the primary credibility signal for researchers moving to industry.
"LLM Engineer | Fine-tuning, RAG systems & AI agents | Helping companies ship GenAI features that work"
The 'that work' qualifier acknowledges the hype cycle and positions this person as a pragmatic practitioner.
Selectively, list 2-3 core tools that are searchable keywords for the roles you want (Python, SQL, Spark, dbt, TensorFlow). Don't list your entire stack; use your About section for that.
Translate model outputs into business metrics: '$X saved', 'X% reduction in fraud', 'X% increase in conversion', 'model serving XM users'. These business-level outcomes are far more compelling than accuracy scores or F1 metrics.
Signal both technical depth and leadership intent: 'Senior Data Scientist | ML platform & team leadership | Transitioning to Head of Data or Director of ML roles'. Being explicit about your ambition helps the right opportunities find you.
