Job Details

Pay rate range - $95/hr. to $98/hr. on W2
100% Remote

Must Have
Applied science
Deep Learning
Real-world experience in recommender systems, transformers, or multi-objective tasks.

REQUIRED SKILLS:

• 6+ years of applied research experience (or 4+ with PHD)
• 3+ years of hands-on experience building, deploying, and monitoring production-grade ML models

Years of Experience: 6+

Role Overview
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

1. Lead the development of deep learning-driven personalization algorithms to deliver tailored user experiences across multiple channels (e.g., website, email, and others).
2. 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.
3. Architect end-to-end ML pipelines for large-scale deep learning models, including data preprocessing, distributed training, model optimization, and real-time inference.
4. Published research, filed patents, and stayed ahead of industry trends in the personalization and customer intelligence / lead scoring domains.
5. Innovate in multi-modal modeling (text, graph, behavioral, and temporal data) to enhance personalization and lead scoring accuracy.
6. Conduct rigorous A/B testing, causal inference, and counterfactual analysis to measure model impact and iterate rapidly.
7. Collaborate with MLOps engineers to streamline model deployment, monitoring, and retraining using tools like AWS SageMaker or MLflow and other internal tools.
8. Participate in science reviews to raise the science bar in our organization. This includes reviewing your work and the work of others.

Qualifications
Basic 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
• 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 large 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), and 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.

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