Lead Data Scientist - Madison, United States - HCLTech

HCLTech Madison, United States

3 weeks ago

Description

HCLTech is looking for a highly talented and self- motivated Competent Level Data Scientist - LLM Focused to join it in advancing the technological world through innovation and creativity.

Job Title: Competent Level Data Scientist - LLM Focused

Job ID:

Position Type: Full-time

Location: Madison, WI, USA (Hybrid)

Job Summary:

A highly skilled and experienced Data Scientist at a competent level with a strong focus on LLM (Large Language Models). The ideal candidate will possess expertise in GenAI, VectorDB, labelling, and annotation techniques. They should demonstrate a deep understanding of GenAI use cases, proficiency in evaluating LLMs, and be capable of effectively managing teams and tasks within a dynamic environment.

Key Responsibilities:

Utilize advanced statistical and machine learning techniques to extract insights from large datasets.
Lead and contribute to the development and optimization of GenAI models, ensuring high accuracy and efficiency.
Collaborate with cross-functional teams to define and refine GenAI use cases and objectives.
Conduct thorough evaluations of LLMs to assess performance, scalability, and suitability for specific applications.
Oversee the labelling and annotation process for training datasets, ensuring data quality and relevance.
Manage and mentor junior team members, providing guidance on best practices and methodologies.
Stay updated on the latest developments in the field of data science, particularly in relation to GenAI and LLMs.
Drive innovation and continuous improvement initiatives within the data science team.

Must-Have Skills:

Strong proficiency in Python, R, and SQL.
Expertise in machine learning frameworks and libraries (e.g., TensorFlow, PyTorch).
Experience with GenAI platforms and tools, including VectorDB.
Demonstrated ability to manage teams and projects effectively, with excellent communication and leadership skills.
Solid understanding of data labelling and annotation techniques, with experience in developing and implementing data labeling pipelines.

Secondary Skills:

Familiarity with natural language processing (NLP) and text analytics.
Experience with cloud computing platforms (e.g., AWS, Azure, Google Cloud).
Publications or contributions to the field of data science and machine learning.

Skills and Qualifications:

Bachelor's degree in computer science, Statistics, Mathematics, or a related field; advanced degree preferred.
Proven experience (X years) in a data scientist role with a focus on LLMs and GenAI.
Ability to analyse complex datasets, derive actionable insights, and communicate findings to stakeholders.
Strong problem-solving skills and a detail-oriented mindset.
Ability to work independently and collaboratively in a fast-paced environment.

· In addition to salary, this position qualifies for benefits like health insurance, paid time off (PTO), 401(K). Exact compensation may vary based on skills, experience, education, and/or to comply with federal or state law.

How You'll Grow

At HCLTech, we offer continuous opportunities for you to find your spark and grow with us. We want you to be happy and satisfied with your role and to really learn what type of work sparks your brilliance the best. Throughout your time with us, we offer transparent communication with senior-level employees, learning and career development programs at every level, and opportunities to experiment in different roles or even pivot industries. We believe that you should be in control of your career with unlimited opportunities to find the role that fits you best.

Equality & Opportunity for All

Representing 165 nationalities across the globe, we pride ourselves on being an equal opportunity employer, committed to providing equal employment opportunities to all applicants and employees regardless of race, religion, sex, color, age, national origin, pregnancy, sexual orientation, physical disability or genetic information, military or veteran status, or any other protected classification, in accordance with federal, state, and/or local law.

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