Software Engineer

Role Summary:
As a Data Scientist, you will play a crucial role in analyzing complex data sets to uncover trends, patterns, and insights that will support strategic decision-making across various departments. You will work closely with cross-functional teams to solve business problems. This role requires a blend of analytical, technical, and communication skills to translate data into actionable strategies.

Key Responsibilities:

NLP Model Development: Design, implement, and optimize NLP models for tasks such as text classification, sentiment analysis, named entity recognition, and machine translation.
Generative AI Projects: Develop and fine-tune generative models (e.g., GPT, T5, BERT) to create conversational agents, content generation systems, and other text-based applications.
Data Analysis and Preprocessing: Process and analyze large datasets to prepare high-quality inputs for NLP and generative models.
Experimentation and Evaluation: Conduct A/B testing and evaluation on model performance, using metrics to assess NLP and generative models effectiveness.
Collaboration with Cross-Functional Teams: Work closely with product, engineering, and design teams to deploy AI models into production environments and ensure alignment with business goals.
Stay Updated on Advances in NLP AI: Keep current on NLP and generative AI research, tools, and best practices, suggesting enhancements and new techniques to keep our AI capabilities competitive.
Data Visualization and Reporting: Create dashboards, reports, and visualizations to communicate insights from NLP models to non-technical stakeholders.

Required Qualifications:

Education: Bachelor s or Master s degree in Data Science, Computer Science, Machine Learning, NLP, or a related field.
Experience: 2+ years of experience in data science with a focus on NLP and/or Generative AI projects.
Technical Skills: Proficiency in Python and relevant NLP libraries (e.g., Hugging Face Transformers, NLTK, spaCy) and deep learning frameworks (e.g., TensorFlow, PyTorch).
NLP Expertise: Experience with NLP model development, including tokenization, embedding, sequence modeling, and transfer learning with large language models.
Generative AI Proficiency: Hands-on experience with generative models like GPT, T5, or similar for text generation/classification tasks.
Communication Skills: Ability to convey complex data and model insights to technical and non-technical audiences.
Problem-Solving Skills: Strong analytical skills and a passion for leveraging NLP and Generative AI to solve business challenges.


Preferred Qualifications:

Experience with big data technologies (e.g., Hadoop, Spark).
Familiarity with cloud platforms (AWS, Azure, Google Cloud) and MLOps practices.
Knowledge of LLM fine-tuning, prompt engineering, or reinforcement learning with human feedback (RLHF).
Previous experience in deploying NLP or generative models in production environments.