Website Phaxis LLC
Phaxis LLC
Company : Phaxis LLC
The Data Scientist role will play a key role in developing AI models and engineering core machine learning and AI products. This individual will be working in a collaborative team environment across machine learning, product management, data engineering, and software engineering teams. The ideal candidate will be passionate about leveraging machine learning techniques to drive innovation and have a strong background in researching and developing AI models.
Responsibilities
Build and integrate AI/ML/DS tools and workflows to address business needs and increase business efficiency.
Support the design, development, training, and deployment of AI/ML models and engineering solutions to solve business problems through a full development and production cycle in the FinTech domain.
Build and leverage new and existing tools for Large Language Model (LLM), Natural Language Processing (NLP), Optical Character Recognition (OCR), and intelligent document processing tasks.
Evaluate and compare the performance of different AI/ML algorithms and models.
Contribute to the improvement of Machine Learning Operations (MLOps) pipelines and procedures to ensure efficiency, scalability, and maintainability.
Ensure the reliability, robustness, and scalability of machine learning models in production environments.
Collaborate with cross-functional teams, including machine learning engineers, product managers and full stack engineers, to deliver scalable machine learning solutions.
Qualifications
4-8 years of experience as a hands-on data scientist or AI/ML engineer in AI/ML/DS fields
Advanced degree in a relevant field such as AI, ML, Data Science, mathematics, or computer science.
Experience building ML and AI models and systems in a production environment in at least Generative AI/LLM or NLP applications
Experience working with LLM, such as GPT-4, Llama 3, Mistral, and other commercial or open-source models in a production environment
Knowledge of NLP techniques, including text data preprocessing (tokenization, stemming, and text normalization, etc.) and information extraction (summarization, and question answering, etc.)
Proficiency in programming languages in Python, and libraries/frameworks like TensorFlow, PyTorch, spaCy and scikit-learn, etc
Strong knowledge of machine learning algorithms and statistical techniques, their limitations, and implementation challenges
Experience with cloud platforms and distributed computing environments, such as AWS, Google Cloud, or Azure
Experience working in a finance or financial technology job with alternative investments
Direct contributions to experiments, including designing experimental details, writing reusable code, running evaluations, and organizing results
Strong problem-solving skills and able to work independently and collaboratively in a fast-paced, agile environment
Strong communication skills and able to effectively articulate technical concepts to both technical and non-technical audiences
Experience with data visualization tools and techniques to effectively communicate and present findings
Publication record as a lead author or essential contributor at top venues such as CHI, NeurIPS, UIST, ICML, ICLR, ACL, EMNLP, CVPR, AAAI, and/or ICAPS
Portfolio of personal projects on Github, BitBucket, Google Colab, Kaggle, etc.
Understanding of regulatory and compliance requirements in the financial industry and their implications for machine learning applications
Experience with software development best practices, including source control (Git), CI/CD pipelines, testing, and documentation
Familiar with database integration principles and practices, including SQL and NoSQL databases and data warehouse solutions, such as Snowflake
Experience with data transformation tools, such as dbt, and orchestration tools such as Airflow
Benefits
The base salary range for this role is $130,000 to $160,000.