The Spec Analytics Intmd Analyst is a developing professional role. Deals with
most problems independently and has some latitude to solve complex problems.
Integrates in-depth specialty area knowledge with a solid understanding of
industry standards and practices. Good understanding of how the team and area
integrate with others in accomplishing the objectives of the sub function/ job
family. Applies analytical thinking and knowledge of data analysis tools and
methodologies. Requires attention to detail when making judgments and
recommendations based on the analysis of factual information. Typically deals
with variable issues with potentially broader business impact. Applies
professional judgment when interpreting data and results. Breaks down
information in a systematic and communicable manner. Developed communication
and diplomacy skills are required in order to exchange potentially
complex/sensitive information. Moderate but direct impact through close
contact with the businesses' core activities. Quality and timeliness of
service provided will affect the effectiveness of own team and other closely
related teams.
Key Responsibilities –
-
Model Development: Assist in the development, fine-tuning, and deployment of
generative AI models and large language models.
-
Data Preparation: Clean, preprocess, and organize large datasets for
training and evaluation purposes.
-
RAG Frameworks – Customize and fine-tune existing / new RAG frameworks to
meet project requirements.
-
Research and Experimentation: Conduct experiments to test and validate
model performance and keep up to date with the latest advancements in the
field of AI and NLP.
-
Performance Optimization: Implement techniques to improve model efficiency,
accuracy, and scalability.
-
Collaborative Projects: Work with cross-functional teams to integrate
AI models into various applications and services.
-
Documentation: Maintain clear and comprehensive documentation of model
architectures, processes, and findings
Qualifications –
-
Bachelor’s degree in computer science, Data Science, Electrical Engineering,
or a related field. A master's degree is a plus.
-
Programming Skills: Proficiency in programming languages such as Python,
and familiarity with machine learning libraries and frameworks (e.g.,
TensorFlow, PyTorch, Hugging Face Transformers).
-
Mathematics and Statistics: Strong understanding of fundamental concepts
in mathematics and statistics, including linear algebra, calculus, and
probability.
-
NLP Knowledge: Basic knowledge of natural language processing techniques
and concepts, such as tokenization, embeddings, and sequence models.
-
Problem-Solving Skills: Ability to analyze complex problems, develop
innovative solutions, and effectively communicate findings.
-
Teamwork: Strong collaborative skills and a willingness to work in a dynamic
team environment.
-
Curiosity and Learning: Eagerness to learn and stay current with the latest
research and trends in generative AI and LLMs.
Preferred Skills –
-
5+ years of relevant experience in data science, machine learning, or a
related field.
-
Previous experience with AI/ML projects, internships, or relevant
coursework.
-
Familiarity with cloud platforms (e.g., AWS, Google Cloud, Azure) and
version control systems (e.g., Git).
-
Experience with deploying machine learning models in a production
environment.
-
Strong communication skills, both verbal and written, and the ability to
explain technical concepts to non-technical stakeholders.
Education:
-
Masters/University degree or equivalent experience