Requirements

Expected technologies

PyTorch

Spark

Hadoop

Operating system

macOS

Our requirements

• 5+ years of relevant work experience with a Bachelor’s Degree or at least 2 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 0 years of work experience with a PhD, OR 8+ years of relevant work experience.
• 5+ years of relevant work experience with a Bachelor’s Degree or at least 2 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 0 years of work experience with a PhD, OR 8+ years of relevant work experience.
• MS or PhD in a quantitative discipline such as Statistics, Data Science, Mathematics, Physics, Operations Research, Engineering, or a related field, with demonstrated strength in machine learning, deep learning, or equivalent practical experience.
• 7+ years of experience applying data science and machine learning to solve business problems, with proficient Python coding skills and deep expertise in statistical analysis.
• Exceptional problem-solving abilities, with experience designing and implementing complex data science solutions.
• Hands-on experience developing and deploying deep learning models using PyTorch, including model architecture design and optimization.
• Strong background in deep learning, including architectures such as Transformers. Experience with Large Language Models (LLMs), natural language processing (NLP), and advanced expertise in time-series modeling techniques.
• Proficiency with big data tools and frameworks (e.g., Spark, Hadoop), and practical experience implementing MLOps practices such as model versioning, automated deployment, and production monitoring.
• Strong understanding of model interpretability techniques, with the ability to analyze, articulate, and justify the decision-making processes of machine learning and deep learning models.

Optional

• Experience working with financial data and building machine learning solutions for financial services, trading, risk, or related applications is desired.
• Publications in recognized machine learning, data mining, or artificial intelligence journals and conferences are a strong plus.

Your responsibilities

• Develop and apply cutting-edge algorithms and models, ranging from classical machine learning to deep learning techniques, including advanced neural network architectures such as Transformers, Graph Neural Networks (GNNs), and other emerging paradigms.
• Pioneer and apply novel data science, deep learning, and AI methodologies to address unique business challenges and drive innovation.
• Stay up-to-date with the latest research in machine learning, deep learning, and neural network architectures, integrating relevant advancements into business solutions.
• Build, experiment with, and implement statistical, machine learning, and deep learning algorithms - including custom techniques as well as industry-standard tools.
• Devise and apply advanced methods for explainability and interpretability of deep learning models, including mechanistic interpretability and model transparency techniques.
• Develop and implement adaptive learning systems, as well as methods for model validation, A/B testing, and robust performance evaluation.
• Collaborate with data engineers, software developers, product teams, and business stakeholders to translate business requirements into impactful machine learning solutions.
• Communicate complex technical concepts, findings, and recommendations clearly to both technical and non-technical audiences.
• Work with both structured and unstructured data, experimenting with in-house and third-party datasets to evaluate their relevance and value for business objectives.
• Automate all stages of the predictive pipeline to streamline development and minimize manual intervention in both development and production environments.