30 metrics for CV of ML Practitioner

There are two ways to present your experience in CV: task-based and achievement based.

🔧 Task-Based CV

Focuses on what you did — your responsibilities or duties in the role.

Example:

✅ Good for:

• Entry-level roles

• Showing what tools or processes you’re familiar with

  
  

❌ Limitation: Doesn’t show impact — it’s hard to tell if you were effective or just busy.

🚀 Achievement-Based CV

Focuses on what you accomplished — the outcomes and results of your work, ideally backed by metrics.

Example:

✅ Good for:

• Showing impact, value, and effectiveness

• Mid-level to senior roles

• Standing out from others with similar skills

❌ Harder to write — you need to reflect on outcomes and quantify results

🔁 Best Practice: Blend Both

• Start with the task (what you did)

• End with the achievement (what changed because of it)

Example (combined):

🎁 Metrics to quantify impact

Many ML practitioners struggle to quantify impact and tend use metrics for ML models in their CV. It's ok to do it if you worked on ground-breaking research, achieved state-of-the-art results and now applying to another research lab. However for most of us, potential next employer is much more interested in the business impact and scale of your work. 30 examples for your inspiration below.

💰 Business Impact

To highlight ROI and real-world results:

• Increased customer retention by X% through churn prediction

• Saved €X/year by automating [manual process]

• Boosted conversion rates by X% using predictive models

• Helped reduce operational costs by X% with anomaly detection

• Identified €X in upselling opportunities via customer segmentation

• Delivered insights that led to a X% increase in revenue / efficiency / user engagement

• Identified key trends that informed strategy change in [team/product]

• Built reports that reduced decision-making time by X%

• Supported campaign planning that resulted in Y% ROI improvement

👥 User or Customer Impact

• Delivered analytics used to support decisions impacting XX customers

• Built a dashboard used daily by XX sales agents across YY regions

• Analyzed product usage for XX active users to inform feature roadmap

• Optimized a marketing funnel resulting in a XX increase in sign-ups across YY countries

🌍 Business / Geographic Scope

• Supported analytics for operations in XX countries

• Helped prioritize features across XX product teams and multiple markets

• Provided insights for a product used in YY cities across Europe

🛠️ Data & Engineering Work

If you’ve done data pipeline or analytics work:

• Cut data processing time by X% with optimized pipeline

• Built a feature store used in X ML projects

• Created dashboards used daily by X stakeholders

• Consolidated multiple data sources, increasing consistency and saving X hours/month

• Automated ETL pipelines, cutting manual data prep by X hours/week

🗃️ Data Volume & Complexity

• Worked with datasets containing XXX rows across YY data sources

• Cleaned and modeled XX terabytes of time series data for forecasting

• Maintained pipelines processing X GB of data daily/weekly

• Integrated XX, YY, and ZZ data into a unified model

📈 Team / Process / Mentorship Impact

If you’ve led or mentored:

• Mentored X junior team members, leading to [promotion/hiring/skill]

• Led cross-functional team of X people to deliver [project]

• Reduced model deployment time from X weeks to Y days

• Automated reports that saved XX hours/week for YY teams

• Built alerting system monitoring XX events/day

By the way, all my mentees get feedback on their CV as part of the mentorship program. If you are not my mentee yet, you can still get a feedback on your CV. Just book "CV Review" session!