Data Scientist

Responsibilities:

• Analyze and interpret structured and unstructured data to uncover insights.

• Utilize data exploration techniques and tools such as Pandas, NumPy, and SQL.

• Collaborate with stakeholders to define key performance indicators (KPIs) and business metrics.

• Develop, train, and evaluate machine learning models to solve business challenges.

• Work with frameworks like Scikit-learn, TensorFlow, or PyTorch.

• Optimize models through hyperparameter tuning and feature engineering.

• Perform data cleaning, transformation, and feature extraction to prepare datasets for modeling.

• Work closely with Data Engineers to ensure data availability and quality.

• Assist in deploying models to production environments on cloud platforms such as GCP.

• Monitor model performance and suggest improvements based on real-world data.

• Work cross-functionally with business teams, product managers, and engineers to deliver data-driven solutions.

• Present findings and recommendations to both technical and non-technical audiences.

• Stay updated with the latest trends in AI and Data Science.

• Participate in internal knowledge-sharing sessions and contribute to team development.

Qualifications:

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or related fields.

• 2-3 years of experience in Data Science, Machine Learning, or related roles.

• Proficiency in Python (e.g., Pandas, NumPy, Scikit-learn)

• Experience with SQL and NoSQL databases (e.g., BigQuery, MongoDB).

• Familiarity with cloud platforms such as Google Cloud Platform (GCP).

• Knowledge of version control systems such as Git.

• Strong analytical, problem-solving, and communication skills.

• Experience in A/B testing and statistical analysis is a plus.

• Understanding of basic MLOps practices and model deployment is an advantage.

• Experience with BI tools such as Looker Studio or Power BI.

• Knowledge of Agile methodologies.

• Exposure to NLP, Computer Vision, or Time Series Analysis.

• Hands-on experience with containerization tools like Docker.