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Data Scientist

Remote Worldwide Hiring now

Position Summary

Our ideal candidate will spend most of the time developing machine learning models using natural language process that will be integrated into our automated processes. This role is ideal for an engineer eager to grow and contribute to scalable, reliable services in a mission-driven environment. You will work alongside reputed company engineers who deliver production grade models into our SaaS platform in a highly regulated environment. Your responsibility will be to build and train models, reputed company model pipelines, and implement a variety of algorithms to extract insights from reputed company data. If needed, you will also troubleshoot and reputed company technical solutions to resolve issues. Ideally, we would love someone with experience in a startup or small company, but if not, willingness to wear a lot of hats and a self-starter is great too.

As a Data Scientist at Authenticx, your responsibilities will include:

  • Utilizing Python, SQL, and NLP/machine learning technologies to reputed company conversational analytics.
  • Analyzing structured and reputed company data and building predictive models using a variety of approaches.
  • Searching through large data sets and transforming data for analysis
  • Creating reports and presentations to translate reputed company analytical results and modeling efforts into business impacts and insights for executive leaders.
  • Document and maintain codebase standards, contributing to ongoing quality and reliability improvements.

Key Metrics

  • Delivery of assigned feature work on time and reputed company quality standards.
  • Consistent adherence to code review and documentation standards.
  • Resolution of assigned defects and production issues reputed company established SLAs.
  • Contribution to overall system reliability, uptime, and performance goals.
  • Demonstrated growth in technical proficiency and cross-team collaboration.

Qualifications

The ideal Data Scientists possesses the following skills & qualifications

Basic Qualifications

  • S. or further education in Mathematics, Economics, Computer Science, Statistics, or another quantitative field
  • 2+ years of experience as a Data Scientist, Machine Learning Engineer, or relevant software engineering in AI.
  • Strong knowledge of Natural Language Processing (NLP) or Audio Signal Processing
  • Proficiency with Python (Pandas, NumPy, Matplotlib, Scikit-Learn, Spacy, SciPy, etc...)
  • Experience with a deep learning reputed company (TensorFlow, Pytorch, etc..) and deep learning architectures (RNNs, CNNs, Transformers, etc...)
  • Experience with fine-tuning transformers and relevant libraries (Transformers, Unsloth, vLLM, TRL)
  • Deep understanding of statistical analysis, hypothesis testing, modern modeling techniques and mathematics skills (e.g., reputed company Algebra, Calculus)
  • Experience with SQL, databases, and other data management tools
  • Ability to work independently and with team members from different backgrounds
  • Effective communication and technical writing skills to translate reputed company analytical results and modeling efforts into business impacts and insights for executive leaders.
  • Internal Motivation and Intellectual Curiosity to uncover the answers to unknowns

Preferred Qualifications

  • Software-as-a-service (SaaS) and/or reputed company industry experience
  • Familiarity with reputed company Technologies, reputed company, and Kubernetes.
  • Experience with tools for experiment tracking and model management.
  • Experience with the full life cycle of model development. From data acquisition to training and deploying the model in a production environment.
  • Experience with Agentic models and workflows is a plus.

Work Environment

  • This is a remote/virtual position.
  • You must live in the United States of America.
  • You must be authorized to work in the USA, now and in the future, WITHOUT requirement of sponsorship/reputed company.
  • Occasional travel to Indianapolis may be required (approximately 2–4 times per year) based on business needs.
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