Data Scientist

Qualifications:

• Bachelor’s degree in Computer Science, Engineering, or a related field.
• 1-3 years of proven experience in Data Science.
• Strong theoretical and practical knowledge of advanced statistical algorithms, including machine learning, deep learning, and optimization techniques, with the ability to apply them effectively to real-world business problems.

Responsibilities:

• Design and develop end-to-end machine learning pipelines, including model development, tuning, implementation, and monitoring for various analytical use cases.
• Collaborate closely with business stakeholders and product management teams to deliver impactful analytics solutions.
• Present insights and analytical results to both technical and non-technical audiences in a clear and compelling manner.
• Continuously explore and evaluate emerging tools and technologies to enhance solution efficiency and scalability.
• Demonstrate outstanding analytical thinking and problem-solving skills.

Mandatory Skills:

Languages & Libraries:

• Python, Pandas, NumPy, Scikit-learn, NLTK, Keras, SQL

Techniques & Concepts:

• Exploratory Data Analysis (EDA)
• Machine Learning and Neural Networks
• Transformers and Natural Language Processing (NLP)
• Model building, hyperparameter tuning, and performance evaluation based on business metrics
• Model deployment and MLOps practices
• Strong foundation in Statistics (e.g., Probability Distributions, Hypothesis Testing)
• Deep Learning techniques and architectures
• Data pipelines and Data Engineering fundamentals

Good to Have:

• Expertise in Large Language Models (LLMs) for solving NLP problems using state-of-the-art models and services (e.g., OpenAI, Claude).
• Familiarity with Retrieval-Augmented Generation (RAG) and techniques to enhance the performance and capabilities of LLM-based solutions.
• Strong logical reasoning and design skills for creating effective, user-centric conversational flows for chatbot applications.
• Passion for research and innovation, with a commitment to staying current on advancements in NLP, LLMs, and ML.
• Experience in data modeling and designing robust data architectures.