Sr Applied Scientist

Stay current with the latest advancements in machine learning, large language models (LLMs), including prompt engineering, finetuning, agentic workflow, and integrating them into our solutions.

Design and implement advanced algorithms and build machine learning models to solve complex business problems, enabling actionable insights.

Analyze large and complex datasets to identify patterns, trends, and correlations that drive datadriven decision making.

Lead endtoend model development processes, including data preprocessing, feature engineering, model selection, and performance evaluation.

Collaborate closely with crossfunctional teams, including product managers, engineers, and data scientists, to translate business requirements into technical solutions.

Utilize your strong programming skills in languages such as Python, Java, or similar, to implement and optimize algorithms and models for scalability and performance.

Conduct extensive experimentation and analysis to assess model performance, identify areas for improvement, and iterate on existing models.

Mentor and provide guidance to junior team members, fostering a culture of continuous learning and knowledge sharing.

Requirements

• Ph.D. or Masters degree in Computer Science, Applied Mathematics, Statistics, or a related field.
  

• 5+ years of relevant work experience.
  

• Experience in a startup company environment is a plus.
  

• Deep passion for data and analytics, with a strong problemsolving and analytical mindset.
  

• Solid background in algorithm design, optimization, and advanced mathematical concepts.
  

• Proficiency in programming languages such as Python, Java, or similar, with a focus on writing efficient and scalable code.
  

• Real world experience designing and deploying agentic workflows using LLMs, such as autonomous agents, tool use (e.g., function calling, retrievalaugmented generation), and multistep reasoning frameworks.
  

• Proven experience in developing and deploying machine learning models in realworld applications.
  

• Extensive knowledge of various machine learning techniques, including supervised and unsupervised learning, deep learning, and reinforcement learning.
  

• Solid understanding of data preprocessing, feature engineering, and model evaluation methodologies.
  

• Excellent communication and collaboration skills, with the ability to convey complex ideas to both technical and nontechnical stakeholders.
  

• Experience working in an agile development environment and a track record of delivering highquality results on time.
  

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  Benefits

  • Stock Options
    

  • PTO
    

  • Paid Holidays
    

  • Medical, Dental, & Vision Benefits
    

  • 401K
    

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