Senior Data Scientist

4 weeks ago

Mumbai, Maharashtra, India Aidetic Full time

Senior Data Scientist

About Aidetic

Aidetic is an AI focused Product Engineering company that designs and implements innovative AI/ML solutions for a wide range of customers in industries like BFSI, Healthcare, Manufacturing, Sports, Education and more.

Led by a team of 4 IITian co-founders, we also have an exclusive advisory board of veterans who are ex-Apple, ex-Google, and ex-Databricks. Aidetic has been building kickass AI solutions before it was cool, and has a very strong foundation to grow fast and become a market leader in the AI/ML consulting space over this decade

About the role

Apply Here: https://app.fabrichq.ai/jobs/867bd788-d86d-4316-91fa-7dbe987dd138

As a Senior Data Scientist, you will focus on developing and implementing advanced data science and machine learning models to support our clients business needs. You'll be solving real world business problems for clients globally using Machine Learning, Deep learning and predictive analytics. On a typical day, youll find yourself working across the end-to-end machine learning lifecycle - from data preparation to model training and deployment. This is a role which prioritizes ownership, excellence, and collaboration.

Experience

4 to 8 years of data science experience

Key Responsibilities

  1. Leading Projects and Architecting AI Systems: Build and lead projects involving creation of advanced AI systems from scratch. Manage junior teammates to navigate projects towards success.
  2. Data Science & Model Development: Design and implement machine learning and deep learning models, utilizing both supervised and unsupervised techniques to address complex business challenges.
  3. Data Preparation & Feature Engineering: Conduct data exploration, preprocessing, and feature engineering to create high-quality datasets and features that enhance model effectiveness.
  4. Advanced Optimization & Evaluation: Apply advanced techniques for hyper-parameter tuning and assess model performance using appropriate evaluation metrics, ensuring model robustness.
  5. MLOps & Deployment: Contribute to the deployment of models with MLOps practices, including versioning, pipeline orchestration, and containerization, ensuring scalability and efficiency.
  6. Collaboration & Communication: Partner with cross-functional teams, including data engineering and business stakeholders, to ensure alignment with client needs and communicate findings effectively.
  7. API Development & Integration: Use RESTful API frameworks to develop, integrate, and scale data-driven applications within client environments.
  8. Continuous Learning & Improvement: Stay current with new data science techniques and industry trends, sharing knowledge with the team and contributing to a culture of innovation.

Qualifications

Mandatory

  1. Qualification: Bachelor's or Master's degree in a relevant field (Computer Science, Data Science, Statistics etc.).
  2. Experience: 4+ years of experience in data science and machine learning.
  3. Leading Projects: Must have experience with leading projects and architecting AI systems from scratch
  4. Programming & Tools: Proficient in Python, Git, and programming IDEs like VS Code or PyCharm.
  5. Machine Learning & Deep Learning: Strong knowledge of traditional machine learning algorithms, deep learning, and advanced hyper-parameter tuning.
  6. Data Manipulation & Analysis: Expertise in data exploration, preprocessing, statistical analysis, feature engineering, and model evaluation.
  7. MLOps & Deployment: Experience with MLOps basics, containerization tools (Docker, Kubernetes), and cloud platforms (AWS, Azure, Google Cloud).
  8. Databases: Familiarity with SQL and NoSQL databases
  9. Soft Skills: Strong analytical thinking, self-motivation, effective communication, and a high degree of ownership over projects.

Good to Know

  1. Proficiency with NLP techniques and familiarity with RAG frameworks like LangChain, LlamaInde
  2. Edge Deployment
  3. Big Data Technologies: Experience with PySpark, Hadoop, etc.
  4. Graph Databases: Knowledge of Neo4j, RDF, or similar technologies.
  5. Messaging Queues: Familiarity with messaging queue systems.

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