Data Scientist

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.

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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.