Data Science Specialist

Key Responsibilities

1. Advanced Model Development:

   Design, build, train, and validate state-of-the-art AI models, with a special focus on Deep Learning, Natural Language Processing (NLP), and Generative AI (including LLMs).

2. Data Analysis & Preprocessing:

   Perform in-depth data exploration, analysis, and feature engineering on large, complex datasets to prepare them for modeling.

3. Problem Formulation:

   Work with stakeholders to understand business problems and translate them into well-defined data science projects.

4. Collaboration:

   Partner closely with AI engineers, product managers, and other data scientists to deploy models into production and integrate them into our products.

5. Model Validation & Iteration:

   Rigorously test and evaluate models for performance, fairness, and robustness. Continuously iterate and improve model performance over time.

6. Innovation & Research:

   Stay current with the latest advancements in AI and machine learning. Apply new techniques and research to solve business challenges.

7. Communicate Insights:

   Clearly communicate complex findings and the value of your work to both technical and non-technical stakeholders.

Qualifications & Skills

• Educational Background:

  Master’s degree in Computer Science, AI, Statistics, or a related quantitative field.

• Professional Experience:

  3+ years of hands-on experience in building and deploying data science and machine learning models.

• Technical Expertise:

• Strong expertise in machine learning, deep learning, NLP, and statistical modeling.
• High proficiency in Python and common AI/ML libraries (e.g., PyTorch, TensorFlow, Scikit-learn, Hugging Face, Pandas).
• Experience with data querying languages (e.g., SQL).

• Problem-Solving:

  Excellent analytical and problem-solving skills with a creative and curious mindset.

• Communication:

  Strong verbal and written communication skills.

Preferred Qualifications

• Experience with large-scale data processing frameworks like Apache Spark.
• Hands-on experience with a major cloud platform (e.g., AWS, GCP, Azure).
• A portfolio of data science projects (e.g., via GitHub, Kaggle).
• Experience working in a fast-paced, product-driven tech environment.