- Lead and assist in building advanced machine learning models, predictive analytics, and statistical methods to address various business challenges.
- Showcase expertise in programming languages like Python or R, with a focus on writing clear, efficient, and maintainable code. Experience with key libraries and frameworks (such as TensorFlow, PyTorch, and scikit-learn) is critical.
- Utilize strong problem-solving abilities to create data-driven solutions, analyze complex datasets, and extract actionable insights that drive meaningful results.
- Collaborate closely with clients to comprehend their business goals, identify opportunities for advanced analytics-driven strategies, and communicate results effectively and in a timely manner.
- Manage the entire model development lifecycle, from defining the problem and exploring data to training, validating, and deploying models.
- Work alongside cross-functional teams, including data engineers, software developers, ML-Ops Engineer and business stakeholders, to integrate analytics solutions into business operations.
- Apply a deep understanding of mathematical and statistical concepts to guide the development and validation of advanced data science models.
Desired Skills and Experience:
- 5-8 years of comprehensive experience in data science and model development.
- Experience in Machine learning Framework like TensorFlow, PyTorch, and scikit-learn etc
- Good to have hands on experience in Data & AI platforms like Databricks,AWS SageMaker etc
- Demonstrate a strong passion for writing high-quality Python code, ensuring it is modular, scalable, and suitable for end-to-end project execution, with active involvement in planning and hands-on work.
- Extensive knowledge of regression and classification techniques, along with the mathematical principles behind them, and proficiency in SQL.
- In-depth understanding of a wide range of data science methodologies, machine learning algorithms, and statistical techniques.
- Excellent communication skills, with the ability to present clearly, articulate ideas, tell compelling data stories, and navigate complex client situations.
