Applied Scientist (Machine Learning)
Impact: Product innovation and competitive differentiation through ML-powered features
Apply machine learning research to real-world product problems, bridging the gap between academic research and production systems. Design and run experiments, build models, and collaborate with engineering teams to ship ML-powered features at scale.
What the day looks like
- People interaction
- Moderate
- Team vs solo
- 60% Team / 40% Solo
- Client facing
- Rarely
- Impact visibility
- High
- Travel
- 5 to 10% for conferences and team offsites
- Schedule flexibility
- Flexible
- Remote work
- Mostly Remote
- Typical work hours
- 45 to 55 hours/week
- Stress level
- High
At a glance
- Median salary
- $165,000
- Entry-level
- $120,000 - $145,000
- Senior
- $220,000+
- Growth by 2033
- 35% (much faster than average)
- Demand
- Growing Fast
- Freelance potential
- Low
- Salary growth potential
- High - 70 to 90% growth from entry to senior
- Typical student debt
- $30,000 - $80,000
Skills you'll use
Hard skills
- Python
- PyTorch
- TensorFlow
- Statistical modelling
- Experimentation design
- SQL
- Distributed computing
Soft skills
- Analytical thinking
- Communication
- Intellectual curiosity
- Collaboration
- Problem-solving
Technical complexity: Very High
How to get there
- Minimum education
- Master's Degree
- Licensing
- No
- Years to mid-career
- 3 to 5 years
- Years to senior
- 6 to 9 years
- Career switching
- Hard
Where this career leads
How people arrive here
Where you can go from here
Typical progression
- Research Intern > Applied Scientist I > Applied Scientist II > Senior Applied Scientist > Principal Applied Scientist > Director of Applied Science
Future outlook
- Automation probability
- 12% low risk as the role itself designs and evaluates automation systems
- AI disruption risk
- Low
- Demand trend
- Growing Fast
How people feel about it
- Overall satisfaction
- 8.2/10
- Meaning
- 8.5/10
- Work-life balance
- 6.8/10
- Prestige
- 8.5/10
- Social perception
- Very High