Applied Scientist IV

Ursus, Inc.; San Francisco (CA); Remote; USD 150,000 - 200,000; Full time

Job summary

Join a leading company as an Applied Scientist IV where you'll leverage deep learning and machine learning to redefine customer engagement. This remote position offers the opportunity to work with state-of-the-art models and drive impactful business outcomes across various channels. Collaborate with cross-functional teams to develop advanced personalization algorithms and predictive scoring models that guide customer interactions. Ideal candidates will possess a PhD or Master's degree, extensive applied research experience, and a strong background in deep learning.

Qualifications

• 6+ years of applied research experience or 4+ with PhD
• 3+ years of hands-on experience building ML models
• Extensive knowledge in machine learning topics

Responsibilities

• Develop deep learning-driven personalization algorithms
• Design predictive lead scoring models
• Architect ML pipelines for large-scale models

Skills

Applied science experience

Deep Learning experience

Statistical analysis

Proficiency in Python

Proficiency in PyTorch

Education

PhD or Master's degree in Computer Science

Tools

AWS SageMaker

MLflow

Job description

JOB TITLE : Applied Scientist IV LOCATION : 100% Remote DURATION : 9 months

PAY RANGE : $86-96 / hour TOP 3 SKILLS :

Applied science experience

Deep Learning experience

Real-world experience in recommender systems, transformers, or multi-objective tasks.

Job Description :

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 Client 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.

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 scale

Publications or patents in applied ML domains

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

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.

Why Join? You'll have a chance to shape the future of AI-driven personalization and customer intelligence at scale, working with a team passionate about blending research with real-world impact. We work with state of the art models, including our internal architectures that exceed SOTA benchmarks.

If you're excited to push boundaries in deep learning while solving high-stakes business problems, we want to hear from you.

IND123

Create a job alert for this search

Applied Scientist • San Francisco, CA, United States