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- Lead Data Science Projects: Oversee the entire lifecycle of data science projects, from data acquisition and preprocessing to model deployment and monitoring, ensuring alignment with client business goals.
- Model Development: Design, implement, and optimize advanced machine learning models and algorithms to address diverse business challenges, including customer insights, risk management, and operational efficiencies.
- Collaboration: Partner with cross-functional teams, including product managers, data engineers, and stakeholders, to identify and capitalize on opportunities for leveraging data and machine learning in various business processes.
- Mentorship: Provide guidance and mentorship to junior data scientists, fostering their development in analytical techniques and best practices.
- Data Analysis: Conduct exploratory data analysis to uncover insights, trends, and anomalies that support strategic initiatives across different sectors.
- Presentation of Findings: Create and deliver presentations that effectively communicate findings and recommendations to both technical and non-technical stakeholders, ensuring clarity and actionable insights.
- Continuous Learning: Stay updated on the latest trends and technologies in data science and machine learning, integrating new methodologies into project workflows to enhance the company’s offerings.
- Experience: 4+ years of hands-on experience in data science, with a proven ability to lead end-to-end projects across various industries, including but not limited to healthcare, finance, and retail.
- Technical Skills: Extensive experience in programming languages, particularly Python and R, is essential. Candidates should demonstrate a deep understanding of machine learning libraries such as scikit-learn, TensorFlow, and Keras, showcasing their ability to implement complex models and algorithms effectively.
- Data Management: Strong experience with data manipulation and analysis tools, including SQL and big data technologies (e.g., Hadoop, Spark).
- Analytical Skills: In-depth knowledge of statistical analysis techniques, model validation methodologies, and performance metrics.
- Soft Skills: Excellent problem-solving abilities, strong communication skills, and a demonstrated capacity to lead and influence cross-functional teams.
