Applied Scientist

Responsibilities

Kforce has an enterprise client seeking an Applied Scientist IV in Seattle, WA. Summary: As an Applied Scientist specializing in personalization, lead scoring, and complex modeling, you will tackle cutting-edge challenges in machine learning and deep learning to redefine how our business engages with customers. You will design and deploy high-impact models that drive customer segmentation, adaptive recommendations, and predictive lead prioritization. Leveraging your expertise in deep learning, NLP, and general modeling, you'll help build solutions that directly influence business outcomes, collaborating with cross-functional teams to turn novel research into scalable, production-grade systems. Responsibilities:

• Lead the development of deep learning-driven personalization algorithms to deliver tailored user experiences across multiple channels (e.g., website, email and others)
• Design and deploy predictive lead scoring models to optimize customer acquisition, conversion, and retention strategies using advanced techniques like survival analysis, graph networks, or transformer-based architectures
• Architect end-to-end ML pipelines for large-scale deep learning models, including data preprocessing, distributed training, model optimization, and real-time inference
• Publish research, file patents, and stay ahead of industry trends in the personalization and customer intelligence / lead scoring domains
• Innovate in multi-modal modeling (text, graph, behavioral, and temporal data) to enhance personalization and lead scoring accuracy
• Conduct rigorous A/B testing, causal inference, and counterfactual analysis to measure model impact and iterate rapidly
• Collaborate with MLOps engineers to streamline model deployment, monitoring, and retraining using tools like AWS SageMaker, or MLflow and other internal tools
• Participate in science reviews to raise the science bar in our organization; This includes reviewing your work and the work of others
  
  

Requirements

• PhD or Master's degree in Computer Science, Statistics, or related field
• 6+ years of applied research experience (or 4+ with PHD)
• 3+ years of hands-on experience building, deploying, and monitoring production-grade ML models
• Applied science experience
• Comprehensive understanding of deep learning concepts
• Proficiency in Python and PyTorch
• Real world experience in recommender systems, transformers, or multi-objective tasks
• Extensive knowledge in a breadth of machine learning topics
• Strong background in statistical analysis, experimental design, and SQL/Spark for big data processing
• Ability to simplify complex concepts for stakeholders
  
  

Preferred Skills

• Proven success in deploying deep learning models (e.g., BERT/Transformers for NLP, diffusion models, GANs or general DNNs) to solve business problems
• Experience working at other companies that operate at a similar scale
• Publications or patents in applied ML domains
  
  

Expertise in at least one focus area in each of the following:

• MLOps: CI/CD pipelines, model monitoring, cloud platforms, Deployment strategy
• Emerging Techniques: LLM fine-tuning, federated learning, automated feature engineering, siamese networks, backbones (feature extraction networks), efficient transformer architectures
  
  

Experience in at least one focus area in either of the following:

• Personalization: Session-based and long term interest recommendations. Two-Tower and Transformer based architectures
• Lead Scoring / Behavior: Predictive analytics, churn modeling, and causal ML for attribution
  
  

The pay range is the lowest to highest compensation we reasonably in good faith believe we would pay at posting for this role. We may ultimately pay more or less than this range. Employee pay is based on factors like relevant education, qualifications, certifications, experience, skills, seniority, location, performance, union contract and business needs. This range may be modified in the future.

We offer comprehensive benefits including medical/dental/vision insurance, HSA, FSA, 401(k), and life, disability & ADD insurance to eligible employees. Salaried personnel receive paid time off. Hourly employees are not eligible for paid time off unless required by law. Hourly employees on a Service Contract Act project are eligible for paid sick leave.

Note: Pay is not considered compensation until it is earned, vested and determinable. The amount and availability of any compensation remains in Kforce's sole discretion unless and until paid and may be modified in its discretion consistent with the law.

This job is not eligible for bonuses, incentives or commissions.

Kforce is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, gender identity, national origin, age, protected veteran status, or disability status.

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