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About This Role
Your wellbeing, our mission. Join a company shaping a healthier world.
GET TO KNOW US
At Wellhub we're revolutionizing workplace wellness. Our platform connects employees worldwide to the best partners for fitness, mindfulness, therapy, nutrition, and sleep—all in one simple subscription. Headquartered in NYC with team members in Europe, North America and South America, we're on a mission to make every company a wellness company.We believe work should be fulfilling, inspiring, and balanced. Here, you'll find a team that values wellbeing, collaboration, and different perspectives, where passion and creativity push boundaries to create real impact. Your contributions will help shape a healthier, more balanced world for you and millions of people globally.
Join us in redefining the future of wellbeing!
THE OPPORTUNITY
We are hiring a Manager Data Scientist to our Engagement \& Platform team in Portugall! This is a Remote – Portugal position, meaning you can work from anywhere within the country. Please note that this role is only open to candidates in Portugal.
As a Data Science Manager, you will be responsible for shaping the ML and experimentation strategy behind our engagement system, including ML models (e.g., contextual bandits, reinforcement learning, classification, etc) and AI agents that produce dynamic, personalized nudges to the Wellhub AI users. You will work closely with Product, Engineering, and Data Scientists to design, build, and evaluate systems that operate at scale. This role also includes people management, mentoring, and raising the bar on scientific rigor across the team.
YOUR IMPACT
- Machine Learning Systems: Design and develop ML models for outreach personalization, including contextual bandits, reinforcement learning, and classification models.
- AI Agents: Design and develop AI agents that generate dynamic, personalized nudge content and conversational interventions.
- A/B Testing \& Experimentation: Design and analyze large\-scale A/B tests and experimentation frameworks to evaluate ML models, data\-driven signals, nudges, and content strategies. Define success metrics and ensure statistically sound experimentation practices.
- Data Pipelines \& Analytics: Collaborate on the design of data pipelines and event schemas that feed the product, ensuring high\-quality data for real\-time decisioning and offline analysis.
- Evaluation \& Monitoring: Define and implement robust monitoring frameworks for the product, including short\-term engagement metrics, longer\-term retention, opt\-outs, and intervention fatigue.
- Leadership \& People Management: Lead, mentor, and grow the data scientists of the team. Set technical direction, ensure high\-quality delivery, and foster strong collaboration with Product and Engineering stakeholders.
- Live the mission: inspire and empower others by genuinely caring for your own wellbeing and your colleagues. Bring wellbeing to the forefront of work, and create a supportive environment where everyone feels comfortable taking care of themselves, taking time off, and finding work\-life wellness.
WHO YOU ARE
- BSc., Master's or PhD degree in Computer Science, Data Science, Machine Learning, Statistics, or a related field;
- At least 8 years of professional experience in Data Science, Machine Learning, or Applied AI roles;
- At least 3 years of experience in people management, leading and developing data science teams;
- Strong proficiency in Python and experience with ML frameworks (e.g., Vowpal Wabbit, PyTorch, or similar);
- Strong foundation in experimentation, A/B testing, and large\-scale experimentation design and analysis;
- Experience working with Generative AI systems in production environments;
- Solid problem\-solving skills and ability to translate product goals into measurable, data\-driven solutions;
- Strong communication skills and ability to work cross\-functionally with Product and Engineering teams.
Preferred Qualifications:
- Experience applying ML techniques such as contextual bandits, reinforcement learning, and classification models in production systems.
- Familiarity with real\-time or near\-real\-time decisioning systems for personalization.
- Experience designing data pipelines or collaborating closely with data engineering teams.
- Background in consumer products or digital health is a plus.
We recognize that individuals approach job applications differently. We strongly encourage all aspiring applicants to go for it, even if they don't match the job description 100%. We welcome your application and will be delighted to explore if you could be a great fit for our team. For this specific role, please note that prior experience in Machine Learning and people management are mandatory requirements.
WHAT WE OFFER YOU
With thoughtful benefits, emotional wellbeing resources, and a culture that empowers you to take ownership of your role and your wellbeing, we create an environment where you can thrive in all dimensions of your life. Our benefits include:
WELLHUB: Free Gold membership with access to onsite gyms and studios, digital fitness programs, and online wellness resources for meditation, nutrition, mental wellbeing support, and more! Add up to three family members to your plan, ensuring access to wellness for those who matter most to you.
HEALTHCARE: Health insurance.
FLEXIBLE WORK: As a Flexible First company, we offer hybrid and remote options to give you the freedom to work in a way that suits you. The model for this specific role can be discussed with your recruiter and hiring manager. We offer all employees a one\-time reimbursement to set up their home office equipment and a monthly work allowance to help cover the costs of working from home.
FLEXIBLE SCHEDULE: Flexibility for us isn't just about where we work—it also means being able to shape how and when we get things done. Together with their leaders, employees define schedules that align with their time zones, team needs, and personal routines.
PAID TIME OFF: We know how important it is to take time away from work to recharge. Employees receive a minimum of 25 days paid holiday per year with an additional day for each year of tenure (up to 5\) in addition to annual holidays (including an extra holiday on your birthday!).
PARENTAL LEAVE: Welcoming a new child is one of the most special moments in your life. Take the time to be present and enjoy your growing family. We offer 100% paid parental leave to all new parents. Parents giving birth are eligible for an extended leave and a ramp\-back period to return part\-time while they get settled.
CAREER GROWTH: Access world\-class platforms, participate in interactive sessions, build your personalized development roadmap, and explore internal opportunities. We focus on continuous learning and feedback to support your journey toward personal and professional success.
CULTURE: You'll join a team of passionate people who come together to break boundaries, support each other, and create a meaningful impact in workplace wellness. We win together, building trust through open communication and a culture where every perspective matters. Learn more about our shared culture and values here.
And to get a glimpse of life at Wellhub… Follow us on Instagram @lifeatwellhub and LinkedIn!
*Wellhub was named one of the Best Engineering Teams and Top IT Companies to Work For in Portugal!* *Join a team where technology, purpose, and wellbeing come together.* *Read more about our award here.*
Diversity, Equity, and Belonging at Wellhub
We aim to create a collaborative, supportive, and inclusive space where everyone knows they belong. At Wellhub, we welcome and celebrate your authentic self.
Wellhub is committed to creating a diverse work environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, religion, color, sex, gender identity or expression, sexual orientation, age, non\-disqualifying physical or mental disability, national origin, veteran status, or any other basis covered by appropriate law.
Our commitment to inclusion also extends to how we recognize and reward our people. We're proud to be Syndio Fair Pay Certified, reflecting our ongoing dedication to equitable and fair pay practices across our global team. Read more about it here.
\#LI\-REMOTE
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Wellhub, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 808 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Wellhub AI Hiring
Wellhub has 4 open AI roles right now. They're hiring across AI Software Engineer, AI Product Manager, AI/ML Engineer, Data Scientist. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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