Ksolves - Senior Data Scientist - Machine Learning

<p>Job Description :<br/><br/>Responsibilities :<br/><br/>- Model Lifecycle Management : Design, train, and optimize machine learning models.

Perform exploratory data analysis (EDA) and data preprocessing, and work efficiently with large datasets.<br/><br/>- MLOps Pipeline Automation : Build, implement, and maintain automated MLOps pipelines for model retraining and monitoring.

Streamline the entire model development and deployment lifecycle for new clients with minimal human intervention.<br/><br/>- Cloud Deployment : Deploy scalable and reliable models into production using AWS services like Amazon SageMaker, Lambda, and EC2.

Utilize Amazon SageMaker Studio Notebooks for developing and deploying models.<br/><br/>- Research & Experimentation : Independently execute end-to-end proofs of concepts (POCs) even in ambiguous scenarios.

<br/><br/>- Explore and evaluate new algorithms, tools, and techniques to improve model performance and address business challenges.<br/><br/>- Collaboration : Work closely with data scientists, UI/backend engineers, and other stakeholders to ensure seamless integration and customer satisfaction.<br/><br/>Requirements :<br/><br/>Education : Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or a related field from a reputed college.<br/><br/>Experience : 2- 3 years of hands-on experience in data science or machine learning.<br/><br/>- Programming & ML Skills : Strong programming proficiency in Python is a must.

Expertise in data manipulation and ML libraries like Scikit-learn and Pandas is required.<br/><br/>- ML Expertise : Strong background in ML, statistical modeling, and deep learning.

Hands-on experience with building and deploying models using frameworks like TensorFlow, PyTorch, Scikit-learn, or Keras.<br/><br/>- (Optional )Optimization and Forecasting : Experience designing and implementing optimization and forecasting models for real-world problems in domains like supply chain, delivery, or resource allocation, using tools such as Gurobi, Google OR-Tools, or CVXPY.<br/><br/>- Cloud & DevOps : Hands-on experience with AI/ML services of cloud platforms, with a strong preference for GCP but also including AWS and Azure.

Experience with Amazon SageMaker, S3, EC2, and Lambda is essential.

Awareness of ML DevOps concepts (versioning, serving, performance tuning, and monitoring) is required.<br/><br/>- Generative AI : Ability to leverage Generative AI tools and platforms, like Github Copilot, Cursor, etc to assist in software development and engineering tasks.<br/><br/>- Tools : Working experience with source control tools like Git/GitHub/GitLab is mandatory.

<br/><br/>Soft Skills : Excellent problem-solving skills to thrive in a fast-paced environment and strong communication abilities to present complex findings to both technical and non-technical audiences.</p> (ref:hirist.tech)