Data Scientist

Responsibilities:

1. Leverage strong ML model experience in complex data environments to achieve business objectives in innovative and efficient ways.
2. Utilize a solid background in mathematics and statistics to inform model development and evaluation.
3. Design, architect, and develop robust machine learning solutions, with a focus on integrating Large Language Models (LLMs) where applicable.
4. Collaborate effectively within Agile Scrum teams, contributing to iterative development and continuous improvement.
5. Document business processes, workflows, and requirements clearly and comprehensively.
6. Engage in close collaboration with various domains within the organization to ensure alignment and understanding of business needs.
7. Participate in collaborative conceptualization sessions to brainstorm and refine project ideas.

Mandatory Skills:

1. Proficiency in Python, Machine Learning, REST API, and SQL.
2. Experience with data processing, cleansing, and verification to ensure data integrity for analysis.
3. Conduct data quality checks and exploratory analyses to inform model development.
4. Demonstrated programming skills in relevant languages, particularly Python and API development.
5. Build end-to-end machine learning models, including data structures and transformation processes.
6. Strong understanding of statistical modeling techniques (e.g., Regression, Clustering, Decision Trees, Logistic Regression).
7. Familiarity with machine learning algorithms (e.g., KNN, Random Forests, Ensemble Methods, Bayesian/Markov Networks).
8. Knowledge of data mining concepts and experience with data visualization tools and dashboards.

Preferred Skills:

1. Experience with Large Language Models (LLMs) such as GPT, BERT, or similar architectures.
2. Understanding of natural language processing (NLP) techniques and their applications in business contexts.
3. Familiarity with advanced research topics, including deep learning, kernel methods, spectral methods, and forecasting.
4. Ability to integrate end-to-end ML solutions into product suites and business functions, with a focus on LLM applications.
5. Design technical frameworks based on various use cases, particularly those involving text data and language understanding.
6. Identify opportunities to automate analytical processes, data extraction, and flow processes, especially in the context of LLMs.
7. Propose hypotheses and design experiments to address specific problems, leveraging LLM capabilities where relevant.

Additional Responsibilities:

1. Stay updated with the latest advancements in machine learning and natural language processing, particularly in the context of LLMs.
2. Mentor junior team members on best practices in machine learning and LLM implementation.
3. Contribute to the development of best practices and standards for machine learning and LLM projects within the organization.