Lead Data Scientist
Content + Source + Freshness • 16 Dec 2025 • 95% confidence
Offer value
High value due to the strategic importance of the role in AI development, competitive salary, and strong career progression opportunities.
- Lead development of AI-driven machine learning systems.
- High compensation reflecting the demand for expertise.
- Opportunities for significant professional impact.
Pros
- Lead role in developing cutting-edge AI solutions.
- High demand for data science skills ensures job security.
- Collaboration with cross-functional teams enhances project impact.
Cons
- High expectations and pressure to deliver results.
- Need to constantly stay updated with fast-evolving ML technologies.
- May involve extensive communication with non-technical stakeholders.
Who it's for
Senior • Remote
Good fit
- Senior data scientists with machine learning experience.
- Individuals adept in technical leadership.
- Professional looking to influence data strategy.
Not recommended for
- Entry-level candidates with no ML experience.
- Those preferring roles with less stakeholder interaction.
- Candidates averse to fast-paced, research-driven environments.
Motivation fit
Key skills
About the job
• Design and deploy end-to-end machine learning systems including NLP models, search and recommendation algorithms, and LLM-based applications.
• Build ML systems that analyze AI search behavior, identify content opportunities, and predict performance across different AI-driven platforms. Create algorithms that help brands understand and optimize for how AI agents discover and rank content.
• Collaborate with product managers to translate business requirements into technical solutions.
Requirements
- 5+ years building production machine learning systems with demonstrated business impact; strong background in NLP and search/recommendation systems required
- Deep expertise across ML approaches: classical models (XGBoost, random forests), modern deep learning architectures (transformers, graph neural networks), and reinforcement learning systems
- Proven ability to take models from research to production, including optimization for latency and cost at scale
- Experience with ML infrastructure and tooling: model serving frameworks, experiment tracking, feature stores, and monitoring systems
- Track record of technical leadership: influencing architecture decisions, improving team practices, and driving cross-functional projects without direct authority
- Excellent communication skills with ability to explain complex technical concepts to non-technical stakeholders and align ML initiatives with business outcomes
🔍 ATS Optimization Keywords
Below are skills and terms extracted directly from this job posting to improve Applicant Tracking System (ATS) visibility. This unique feature helps candidates tailor their applications more effectively — a feature exclusive to JobTailor job listings.
Hard Skills
- machine learning
- natural language processing
- search algorithms
- recommendation algorithms
- XGBoost
- random forests
- transformers
- graph neural networks
- reinforcement learning
- model optimization
Soft Skills
- technical leadership
- communication skills
- collaboration
- influencing architecture decisions
- aligning initiatives with business outcomes
