Data Science Specialist
1 day ago
Key Responsibilities
- 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). - Data Analysis & Preprocessing:
Perform in-depth data exploration, analysis, and feature engineering on large, complex datasets to prepare them for modeling. - Problem Formulation:
Work with stakeholders to understand business problems and translate them into well-defined data science projects. - Collaboration:
Partner closely with AI engineers, product managers, and other data scientists to deploy models into production and integrate them into our products. - Model Validation & Iteration:
Rigorously test and evaluate models for performance, fairness, and robustness. Continuously iterate and improve model performance over time. - Innovation & Research:
Stay current with the latest advancements in AI and machine learning. Apply new techniques and research to solve business challenges. - 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.
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