Applied Data Scientist / Machine Learning Engineer

WorkWave•Holmdel Township, NJ
•Hybrid

About The Position

WorkWave is the leading provider of cloud-based software solutions to pest control, lawn care, landscape management, and other green industries. Our special sauce is our team: We’re a group of makers, doers, creative thinkers, and hard workers, and we’re always looking for individuals who embrace those ideals to come and help us grow. When you become a part of this company, you’ll become part of a dynamic, friendly, fun, and forward-looking community.

Requirements

  • 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.
  • 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.
  • Strong understanding of supervised learning, forecasting, ranking, recommendation systems, optimization, or statistical modeling.
  • Experience with real-world, imperfect product datasets is essential.
  • 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).
  • Hands-on experience operating within cloud environments (AWS, GCP, or Azure).
  • Excellent communication skills with the ability to explain complex technical trade-offs clearly to product, engineering, and non-technical business stakeholders.

Nice To Haves

  • 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.

Responsibilities

  • Drive the development of machine learning capabilities (forecasting, recommendation, ranking, optimization, or decision intelligence) powering customer-facing SaaS products.
  • Design reliable data and feature pipelines alongside models from discovery through experimentation, validation, deployment, and monitoring.
  • Partner with Product Managers and Software Engineers to embed ML directly into product workflows, user experiences, and decision-making tools.
  • Move quickly from prototype to production while balancing accuracy, interpretability, latency, maintainability, and business impact.
  • 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.
  • Collaborate with Data teams to ensure models are supported by high-quality features, while building feedback loops so product experiences improve over time.
  • Help manage and optimize cloud data infrastructure, ensuring trustworthy insights and proactively managing data health before it impacts users.
  • 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.
  • Clearly communicate what ML can and cannot solve to influence roadmap decisions, helping identify where machine learning can create true product differentiation.
  • Guide and mentor other data scientists, ML engineers, analysts, and cross-functional partners in applied ML best practices.

Benefits

  • health and dental
  • 401k with company match
  • Flexible Time Off policy or generous PTO plan (role dependent)
  • paid holidays
  • 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
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