Remote Data Scientist

Summary

Join Ardent as a Data Scientist and play a critical role in analyzing complex unstructured data sets, extracting valuable insights, and driving data-driven decision-making for product and service development. As a remote/hybrid position located in San Juan, Puerto Rico, you will work closely with cross-functional teams to solve challenging business problems, develop AI/ML models, and contribute to the growth and success of our company.

Requirements

• Ph.D. or Master's Degree in Data Science or a related field
• Proven experience as a Data Scientist, with 2+ years (with Ph.D) or 5+ years (with Master's Degree) of relevant work experience
• Proficiency in programming languages such as Python or R
• Strong knowledge of NLP and CV libraries (e.g., spaCy, NLTK, OpenCV) and machine learning frameworks (e.g., TensorFlow, PyTorch)
• Solid understanding of statistical analysis, hypothesis testing, and A/B testing
• Experience with data visualization tools and libraries (e.g., Matplotlib, Seaborn, Plotly)
• Experience with researching and developing AI/ML models
• Familiarity with SQL and database management systems
• Strong problem-solving skills and the ability to work independently and collaboratively
• Excellent communication and presentation skills
• Attention to detail and a commitment to producing high-quality work

Responsibilities

• Conduct in-depth analysis of unstructured data, including text and image data, to identify trends, patterns, anomalies, and biases
• Clean, preprocess, and transform raw unstructured data into usable formats for analysis
• Utilize NLP and CV techniques to extract insights from data
• Research and develop AI/ML models, and algorithms focusing on NLP and CV applications
• Fine-tune and optimize models for improved performance across metrics
• Implement and deploy models into production systems when necessary
• Implement explainable AI algorithms to allow stakeholders to comprehend and trust model outputs
• Create informative and visually appealing data visualizations and dashboards to communicate findings
• Use tools like Grafana, or similar, to present data-driven insights to stakeholders
• Collaborate with cross-functional teams, including engineers, product managers, and business analysts
• Provide data-driven recommendations to support decision-making processes
• Ensure models and AI solutions adhere to ethical guidelines and responsible AI practices
• Monitor and mitigate biases in data and models to promote fairness and transparency
• Stay up-to-date with the latest advancements in NLP, CV, and responsible AI
• Apply new techniques and technologies to improve existing models and processes