Role Description
Data Scientist
Job Summary
We are looking for a results-oriented Data Scientist with expertise in Statistics, Economics, Machine Learning, Deep Learning, Computer Vision, and Generative AI. The ideal candidate will have a proven track record of building and deploying predictive models, conducting statistical analysis, and applying cutting-edge AI techniques to solve real-world business challenges.________________________________________
Key Responsibilities
- Develop models for regression, classification, clustering, and time series forecasting.
- Perform hypothesis testing and statistical validation to support data-driven decisions.
- Build and optimize deep learning models (ANN, CNN, RNN including LSTM, BERT).
- Implement computer vision solutions using YOLOv3, SSD, U-Net, R-CNN, etc.
- Apply Generative AI and LLMs (GPT-4, LLaMA2, Bard) for NLP and content generation.
- Create interactive dashboards and applications using Streamlit or Flask.
- Deploy models using AWS SageMaker, Azure, Docker, Kubernetes, and Jenkins.
- Collaborate with cross-functional teams to integrate models into production.
- Handle large datasets using SQL/NoSQL and PySpark.
- Stay updated with the latest AI/ML research and contribute to innovation.
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Technical Skills
Programming & Frameworks:
- Languages: Python
- Libraries/Frameworks: TensorFlow, PyTorch, Keras, Flask, Transformers, Langchain, PySpark, Caffe
- Visualization & Data Tools: Pandas, NumPy, Seaborn, Matplotlib, Scikit-learn, Scipy, NLTK, Streamlit, OpenCV, Scikit-Image, Dlib, MXNet, Fasta
ML & Statistical Techniques
- Regression (Linear/Logistic), Decision Trees, Random Forest, KNN, Naïve Bayes
- Clustering (KMeans, Hierarchical), Time Series Forecasting
- Hypothesis Testing, Statistical Inference
Deep Learning & Computer Vision
- ANN, CNN, RNN (LSTM, BERT), VGGs, YOLOv3, SSD, HOGs, DCGAN, U-Net, R-CNN, NEAT, Inpainting
Gen-AI / LLMs
- HuggingFace, GPT-4, Bard, LLaMA2, Pinecone, Palm, GenAI Studio, OpenAI fine-tuning
Deployment & DevOps
- AWS (SageMaker), Azure, Docker, Kubernetes, Jenkins, Git, GitHub, API integration
Databases & Tools
- MySQL, NoSQL
- Jupyter Notebook, Google Colab, Visual Studio, Power BI
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Qualifications
- Bachelor’s or Master’s in Statistics, Economics, Computer Science, Data Science, or related field.
- Demonstrated experience in developing and deploying models in production.
- Strong analytical and statistical skills.
- Excellent communication and collaboration abilities.