Skillful data analysts are crucial in today's tech-driven landscape. The role of a Data Scientist involves leveraging advanced statistical techniques and machine learning algorithms to derive actionable insights from complex datasets.
Your Key Responsibilities:
1. Develop innovative predictive models, collaborating closely with the data team to design and implement scalable solutions.
2. Integrate large language models into data pipelines and applications using LLM APIs.
3. Evaluate different LLMs based on factors such as accuracy, latency, and cost-performance trade-offs.
4. Analyze large datasets utilizing state-of-the-art machine learning technologies.
5. Automate data processing workflows and ML model pipelines to optimize efficiency.
6. Write efficient, reusable, and scalable code to improve AI-driven processes.
Necessary Qualifications and Skills:
* 2 years of experience in a similar role or equivalent expertise.
* A STEM degree (Engineering, Physics, Mathematics, Statistics) and strong knowledge of statistical testing & inference for KPI extraction from Big Data.
* Solid expertise in SQL and Python programming languages.
* Familiarity with data management and numerical computing libraries (Pandas, Dask, NumPy, SciPy, Spark, etc.).
* Experience with ML engines (Jupyter, Google Colab) and Machine Learning frameworks (Scikit-learn, NLTK, spaCy).
* Hands-on experience in data preparation, feature extraction, and engineering.
* Proficiency in working with LLM APIs (OpenAI GPT, Claude, Mistral, Gemini, Cohere, etc.).
* Understanding of RESTful APIs, including authentication, rate limits, and response parsing.
* Experience with model selection and training, particularly transfer learning.
Bonus Skills:
* Big Data experience and knowledge of causal inference for spatial and time-series problems.
* Experience with geospatial analysis for location intelligence and familiarity with transformer architectures like BERT (Hugging Face).
* Understanding of LLM deployment, latency optimization, and cost-efficient scaling strategies.
* Hands-on experience with deep learning frameworks (TensorFlow, Keras, PyTorch) and proficiency in Git version control best practices.
* Experience with Agile methodologies and AWS Cloud infrastructure.