Applied Data Scientist / Machine Learning Engineer (Decision Intelligence)

About the Role

We are seeking a product-oriented Applied Data Scientist or Machine Learning Engineer to design, launch, and grow ML-driven products that help customers make better decisions, run their operations more effectively, and improve the experience they deliver to their own users.

This position is not focused on pure research, and it is not an internal analytics role. We need someone who has taken machine learning from the point of problem framing all the way through experimentation, production release, measurement, iteration, and long-term stewardship. Strong models matter, but only when they are deployable, understandable, measurable, scalable, and genuinely useful inside a real product.

Whether your strengths are closer to data engineering/ML operations or applied data science, you should be motivated to ship solutions quickly and to connect these disciplines in order to bring AI into production.

What You Will Do

Engineering and AI Enablement

• Own machine learning work end to end, including capabilities such as forecasting, recommendations, ranking, optimization, and decision intelligence that support customer-facing SaaS products.
• Build dependable data and feature pipelines, then develop models through discovery, experimentation, validation, deployment, and monitoring.
• Work closely with Product Managers and Software Engineers to weave ML into product flows, user experiences, and decision-support tools.
• Prototype efficiently and move solutions into production while balancing accuracy, interpretability, latency, maintainability, and business value.

Ecosystem Ownership and Strategy

• Create offline and online evaluation approaches, including quality checks, drift assessment, and reliability measurement.
• Design A/B tests and causal measurement methods to demonstrate that ML features improve customer outcomes.
• Coordinate with data teams to keep models supported by strong features and to establish feedback loops that improve product performance over time.
• Support cloud data infrastructure and help maintain trustworthy data health before issues affect users.

Product and Technical Direction

• Apply sound judgment to choose between traditional ML, statistical approaches, LLMs, heuristics, or simpler product logic.
• Help guide roadmap decisions by clearly explaining what ML can solve, what it cannot, and where it can create meaningful product differentiation.
• Mentor data scientists, ML engineers, analysts, and cross-functional partners on practical applied ML practices.

What We Are Looking For

• You have already delivered machine learning in real products and are comfortable taking an ambiguous business problem, deciding whether ML is the right answer, building the solution, and proving its impact.
• You think like a product owner as much as a model builder, and you understand that trust, adoption, explainability, and speed can matter more than a small gain in accuracy.
• You recognize that strong models depend on strong pipelines, and you value usability, time to insight, and customer confidence alongside code efficiency.

Requirements

• At least 3 years of professional experience, preferably 5 or more, in applied data science, machine learning, or ML engineering, with direct experience building and shipping production models. Experience in SaaS is especially valuable.
• Strong Python ability, plus hands-on use of applied ML tools and frameworks such as Scikit-Learn, XGBoost, PyTorch, or TensorFlow. Solid SQL skills are required.
• Deep understanding of supervised learning, forecasting, ranking, recommendation systems, optimization, or statistical modeling, including work with messy real-world product data.
• Working knowledge of MLOps concepts such as model versioning, feature pipelines, orchestration with Airflow/dbt/Dagster, monitoring, and drift detection, along with modern data platforms like Snowflake, BigQuery, Redshift, or Databricks.
• Hands-on experience in at least one cloud environment: AWS, GCP, or Azure.
• Strong communication and collaboration skills, with the ability to explain technical trade-offs to product, engineering, and non-technical stakeholders.

Nice to Have

• Background in decision intelligence, forecasting, customer behavior modeling, workforce or route optimization, or operational intelligence products.
• Experience applying LLMs, GenAI, or agentic workflows to real product use cases.
• Prior work as a Senior or Lead scientist guiding technical direction.

About the Team and Company

WorkWave describes itself as a laid-back but polished organization with a casual environment, remote flexibility, and a strong focus on building creative, innovative, best-in-class solutions that help customers succeed.

The company emphasizes care for clients, trust-based partnerships, teamwork, collaboration, curiosity, and a fun, energetic culture. It also supports a remote-first global work community where ideas matter and growth is prioritized.

WorkWave operates with a hybrid-friendly mindset: employees may work remotely or use office space when needed for cross-training, team building, or brainstorming. Its headquarters are in Bell Works in Holmdel Township, New Jersey, and the company has employees across more than 30 states, 7 countries, and multiple regional offices.

Benefits and Perks

• Comprehensive benefits package with health, dental, and a 401(k) plan with company match.
• Flexible Time Off or a generous PTO plan, depending on the role, along with paid holidays.
• Up to 4 weeks of paid bonding leave.
• Tuition reimbursement.
• Employee Assistance Program through TotalCare, including free 24/7/365 counseling plus financial counseling, legal guidance, adoption assistance, and related services.
• 24/7 virtual medical access through Teladoc.
• Quarterly peer-nominated awards.
• Regional discounts and perks.
• Opportunities to take part in charitable events and community giving.
• Access to extensive on-demand training libraries and live training sessions throughout the year.

Additional Information

WorkWave encourages current employees with strong product knowledge and enthusiasm for the company’s mission to consider internal growth opportunities.

The company has received multiple workplace and industry recognitions, including being named Best Place to Work in New Jersey 10 times by NJBiz, as well as acknowledgments such as the Inc. 5000, SaaS Award, IT World Awards, Globe Awards, Silver Stevie Award for Employer of the Year, Best Place to Work by Inc. Magazine, and recognition in The Software Report’s Top 100 Software Companies of 2022.

The company is an equal opportunity employer and considers applicants without regard to race, color, age, religion, sex, sexual orientation, gender identity, national origin, veteran status, or disability status.

WorkWave also notes that AI tools may be used to assist parts of the hiring process, such as resume review and application analysis, but final hiring decisions are made by people.

Regarding pay, the posting states that WorkWave supports salary transparency, but no specific compensation range is given for this role in the provided information. The listed salary range referenced in the posting applies to sales roles and should not be treated as this position’s pay.

Who Should Apply

This role is intended for candidates who already have real-world experience shipping machine learning into production products, especially those who can combine technical depth with product judgment and cross-functional collaboration.