# Applied Data Scientist / Machine Learning Engineer (Decision Intelligence) Remote from[USA](https://jobicy.com/job-region/usa.md)Annual salary Undisclosed Salary information is not provided for this position. Check our [Salary Directory](https://jobicy.com/salaries.md) to estimate the average compensation for similar roles.Department [Data Science & Analytics](https://jobicy.com/categories/data-science.md) Employment type Full Time, Job posted23 Jun 2026Apply before23 Jul 2026Experience level Midweight Views / Applies 1298 / 92 [About company](https://jobicy.com/company/workwave.md) * Share About [WorkWave](https://jobicy.com/company/workwave.md) The Leader in Cloud-Based Field Service and Fleet Management Solutions for Companies With a Mobile Workforce. [Computer Software](https://jobicy.com/company-category/software.md) * 1984 Verified job posting This job post has been [manually reviewed](https://jobicy.com/tools/help-center/employee/how-does-jobicy-verify-the-legitimacy-of-remote-job-listings.md) for authenticity and compliance. ### AI Summary This is a senior-level Applied Data Scientist / ML Engineer role focused on decision intelligence, requiring end-to-end ownership of ML products from problem definition to production. The ideal candidate has strong Python and ML framework skills, experience shipping models into real products, and a product-first mindset. Responsibilities include building pipelines, collaborating with product teams, and mentoring others. The role values practical trade-offs, explainability, and customer impact over pure model accuracy. It is suited for someone who thrives in a fast-paced, cross-functional environment with high autonomy. ### Role DNA Job Complexity Easy Hard Pace & Pressure Relaxed Fast-paced Autonomy Level Guided Full Ownership Communication Load Independent Highly Collaborative AI Insight The role requires senior-level experience (3-5+ years) with end-to-end ML ownership, product integration, and mentorship, which demands advanced technical and collaboration skills. However, it is not a research-only position, making it challenging but not the highest difficulty. ### Salary Analysis Median Highly Competitive $150,000US Market $100k – 200k 0 $220k AI Insight The salary information is not provided in the posting, but based on US market data for a senior Applied Data Scientist / ML Engineer (Decision Intelligence), the median salary is approximately $150,000. This role typically commands a range between $100,000 and $200,000 depending on location, experience, and company. The offered salary should be competitive to attract top talent with product-building experience. ### Core Skills Required [Machine Learning](https://jobicy.com/jobs?search_keywords=Machine+Learning.md) [Python](https://jobicy.com/jobs?search_keywords=Python.md) [SQL](https://jobicy.com/jobs?search_keywords=SQL.md) [MLOps](https://jobicy.com/jobs?search_keywords=MLOps.md) [Data Pipelines](https://jobicy.com/jobs?search_keywords=Data+Pipelines.md) [Product Management](https://jobicy.com/jobs?search_keywords=Product+Management.md) [A/B Testing](https://jobicy.com/jobs?search_keywords=AB+Testing.md) [Mentorship](https://jobicy.com/jobs?search_keywords=Mentorship.md) [SaaS](https://jobicy.com/jobs?search_keywords=SaaS.md) [Decision Intelligence](https://jobicy.com/jobs?search_keywords=Decision+Intelligence.md) ### Cover Letter Sample I am excited to apply for the Applied Data Scientist / Machine Learning Engineer (Decision Intelligence) position at your company. With over 5 years of experience in end-to-end ML product development, I have a strong track record of shipping models that drive real customer impact. My background includes building scalable pipelines, collaborating with product teams, and owning ML solutions from conception to monitoring. I am particularly drawn to this role because it emphasizes product impact and practical decision-making. I understand that great models are not just accurate but also explainable and usable. In my previous role, I led the development of a recommendation system that improved user engagement by 20%, balancing model complexity with interpretability. I possess strong Python, SQL, and ML framework skills, and I am comfortable mentoring team members and influencing product roadmaps. I am eager to bring my bias toward shipping and my product-first mindset to your team. Thank you for considering my application. I look forward to the opportunity to discuss how I can contribute to building AI-powered decision intelligence products. Copy ### Sample Interview Questions Describe a time when you took an ML project from problem definition to production deployment. What challenges did you face and how did you measure success?In a previous role, I led the development of a demand forecasting model for a SaaS product. I started by defining the problem with product managers, then built feature pipelines and trained an XGBoost model. Challenges included data quality and latency requirements. We measured success through offline metrics (MAE) and online A/B tests, achieving a 15% reduction in inventory costs. The model was deployed using a microservice with monitoring for drift.How do you decide between using a complex ML model versus a simpler heuristic or business rule?I evaluate based on business impact, interpretability, and maintenance cost. For high-stakes decisions where explainability is critical, I may opt for a simpler linear model or rule-based system. If the problem requires capturing complex patterns and the team has MLOps support, I consider more complex models like gradient boosting or neural networks. I always prototype quickly and compare against a baseline to justify the complexity.Explain how you have mentored other data scientists or engineers. What approach do you take?I believe in hands-on mentoring through code reviews, pair programming, and structured learning sessions. I focus on building strong fundamentals in ML modeling, testing, and pipeline design. I also encourage a product mindset by having mentees participate in cross-functional meetings. Regular feedback and setting clear goals have helped my mentees grow into independent contributors.How do you ensure that an ML model remains reliable and valuable after deployment?I implement continuous monitoring for data drift, model drift, and performance metrics. Automated alerts trigger retraining or investigation. I also set up feedback loops to collect user interactions and improve the model over time. Regular evaluation against a holdout set and business KPIs ensures the model stays aligned with product goals.Describe a situation where you had to convince a non-technical stakeholder to invest in ML infrastructure. How did you communicate the value?I prepared a cost-benefit analysis showing how improved data pipelines would reduce time-to-insight and enable faster model iterations. I presented a prototype that demonstrated a potential revenue uplift. By using concrete examples and tying ML infrastructure to business outcomes (e.g., customer retention), I gained buy-in from leadership and secured budget for MLOps tools. We are looking for a product-minded Applied Data Scientist or Machine Learning Engineer to help build, ship, and scale ML-powered products that directly improve how our customers make decisions, operate their businesses, and serve their own users. This is not a research-only role, nor is it a service-oriented internal analytics position. We want someone who has taken machine learning from problem definition through experimentation, production deployment, measurement, iteration, and long-term ownership. You understand that great models are not just accurate in notebooks—they are usable, explainable, measurable, scalable, and valuable inside a real product. Whether your background leans heavily toward Data Engineering/ML Ops or Applied Data Science, you have a strong bias toward shipping and an interest in bridging both worlds to bring AI to life. ## WHAT YOU’LL DO: Engineering & AI Enablement * End-to-End ML Ownership: Drive the development of machine learning capabilities (forecasting, recommendation, ranking, optimization, or decision intelligence) powering customer-facing SaaS products. * Pipeline & Model Development: Design reliable data and feature pipelines alongside models from discovery through experimentation, validation, deployment, and monitoring. * Product Integration: Partner with Product Managers and Software Engineers to embed ML directly into product workflows, user experiences, and decision-making tools. * Pragmatic Prototyping: Move quickly from prototype to production while balancing accuracy, interpretability, latency, maintainability, and business impact. Ecosystem Ownership & Strategy * Evaluation & Experimentation: Define offline and online evaluation strategies, including model quality, drift, and reliability. Design A/B tests and causal measurement frameworks to prove ML features improve customer outcomes. * Data Health & Feedback Loops: Collaborate with Data teams to ensure models are supported by high-quality features, while building feedback loops so product experiences improve over time. * Platform & MLOps Support: Help manage and optimize cloud data infrastructure, ensuring trustworthy insights and proactively managing data health before it impacts users. Product & Technical Direction * Strategic Judgment: Bring strong judgment around when to use traditional ML, statistical modeling, LLMs, heuristics, or simpler product logic. Make practical trade-offs across model complexity and customer impact. * Roadmap Influence: Clearly communicate what ML can and cannot solve to influence roadmap decisions, helping identify where machine learning can create true product differentiation. * Mentorship: Guide and mentor other data scientists, ML engineers, analysts, and cross-functional partners in applied ML best practices. ## WHO YOU ARE: * The Proven Builder: You have shipped ML into real products. You are comfortable starting with an ambiguous product problem, figuring out if ML is the right solution, building it, and measuring whether it worked. * Product-First Architect: You care about product impact as much as model performance. You know that a model with slightly lower accuracy but higher trust, faster inference, better explainability, and stronger user adoption is the better product decision. * A Multi-Disciplinary Executioner: You understand that a model is only as good as the pipeline feeding it. You prioritize usability, “Time to Insight,” and customer trust as much as you do code efficiency. ## WHAT YOU’LL BRING: * Experience: 3+ years (ideally 5+) of professional experience in applied data science, machine learning, or ML engineering, including hands-on experience building and shipping models into production products. Experience with SaaS products is highly valued. * Technical Core: Strong Python skills and hands-on experience with applied ML libraries and frameworks (e.g., Scikit-Learn, XGBoost, PyTorch, TensorFlow). Solid SQL expertise is required. * ML & Modeling Depth: Strong understanding of supervised learning, forecasting, ranking, recommendation systems, optimization, or statistical modeling. Experience with real-world, imperfect product datasets is essential. * Ops & Orchestration: Familiarity with MLOps concepts (model versioning, feature pipelines, orchestration via Airflow/dbt/Dagster, monitoring, drift detection) and modern data platforms (e.g., Snowflake, BigQuery, Redshift, Databricks). * Cloud Infrastructure: Hands-on experience operating within cloud environments (AWS, GCP, or Azure). * Communication & Collaboration: Excellent communication skills with the ability to explain complex technical trade-offs clearly to product, engineering, and non-technical business stakeholders. ## BONUS POINTS FOR: * Experience with decision intelligence, forecasting, customer behavior modeling, workforce/route optimization, or operational intelligence products. * Experience with LLMs, GenAI, or agentic workflows applied to real product use cases. * Prior experience acting as a Senior or Lead scientist responsible for guiding technical direction. WHAT YOU SHOULD KNOW ABOUT US: • We are laid back but buttoned up. We offer a casual work environment and remote work flexibility and have a passion for developing creative, innovative best in class solutions that directly contribute to the success of our customers• We care deeply and deliver service and solutions that make a real difference in the lives of our clients and their businesses• We openly accept others as they are and build strong partnerships based on trust• Teamwork and collaboration is key to help our colleagues and customers solve their challenges• Our team is energetic, fun, naturally inquisitive and eager to make an impact, we invite you to join us! LOVE WHAT YOU DO, NO MATTER WHERE YOU DO IT: • Join our Remote-First Global Work Community: WorkWave provides an innovative and dynamic remote-first Global Work Community that encourages growth, creativity, and collaboration. No matter what stage of your career or where you live, WorkWave is your place to be part of a global company with a startup feel, where your ideas matter and your growth is a priority. A GLOBAL COMPANY WITH A LOCAL PRESENCE: • We know that there are benefits of being in the office and working from home. WorkWave promotes a healthy work/life balance and provides employees with the flexibility of collaborating in the office or the option to work virtually if desired. Our teams are well versed at working collaboratively in a fully virtual environment.• Our HQ is based at our state of the art home office in the historic Bell Works complex located in Holmdel Township, New Jersey. We keep our offices available to all to use when working remotely isn’t feasible, or to help with cross training, team building and/or brainstorming. • We have employees in over 30 states, 7 countries and many regional offices – each with their own set of perks and opportunities to give back to the local community.• Whether you work remotely or take advantage of one of our offices, you’ll find a community of WorkWavers that value diversity, and care deeply about our products, clients, our communities and each other. RELAX, WE’VE GOT YOU COVERED: • Employees can expect a robust benefits package, including health and dental and 401k with company matchAND BEYOND…• Find your perfect work/life balance with our Flexible Time Off policy or generous PTO plan (role dependent) and paid holidays• Up to 4 weeks paid bonding leave• Tuition reimbursement• Robust Employee Assistance Program through TotalCare offering free counseling 24/7/365, plus financial counseling, legal guidance, adoption assistance services and much more!• 24/7 access to virtual medical care with Teladoc• Quarterly awards based on peer nominations• Regional discounts and perks• Opportunities to participate in charitable events and give back to the community GROW WITH US: • We understand the impact of attracting and keeping top talent and reward intellectual curiosity and a thirst for personal and professional growth• Encouraging our employees that already have an intimate knowledge of and passion for our products to apply for other roles within our walls just makes sense!• Our employees have access to extensive video libraries for soft skill and role specific training available 24/7 and live trainings are provided throughout the year JOIN OUR WINNING TEAM! • 10 Time winner of Best Place to Work in New Jersey by NJBiz!• WorkWave has been recognized with multiple awards for its outstanding products, growth and culture, including the Inc. 5000, SaaS Award, IT World Awards, Globe Awards, Silver Stevie Award for Employer of the Year, and Best Place to Work Inc. Magazine• Named one of The Software Report’s 3rd annual list of the Top 100 Software Companies of 2022 (worldwide!) We’re an equal opportunity employer. All applicants will be considered for employment without attention to race, color, age, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status: Don’t meet every single requirement? Studies have shown that women and people of color are less likely to apply to jobs unless they meet every single qualification. At WorkWave, we are dedicated to building a diverse, inclusive and authentic workplace, so if you feel like you could make a great impact in this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. You may just be the right candidate for this or other roles! Show more [Apply now >](https://jobicy.com/jobs/147495-applied-data-scientist-machine-learning-engineer-decision-intelligence.md) > Annual salary information is not provided for this position. 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