What you'll do :

• Lead end-to-end data science projects from data collection to model deployment and monitoring.
• Define project goals, milestones, and deliverables, ensuring alignment with business objectives.
• Mentor and provide guidance to junior data scientists and team members.
• Collect data from diverse sources, including databases, APIs, and web scraping.
• Clean, preprocess, and organize raw data to ensure quality and usability.
• Build and maintain data pipelines for efficient data flow and storage.
• Conduct comprehensive EDA to understand data patterns, distributions, and potential issues.
• Visualize data using tools like Matplotlib, Seaborn, or Tableau to derive insights.
• Identify and create relevant features to enhance model performance.
• Apply techniques such as feature scaling, normalization, and encoding.
• Build and train machine learning models using supervised and unsupervised techniques.
• Implement advanced statistical and machine learning algorithms to solve complex business problems.
• Evaluate model performance using appropriate metrics and validation techniques.
• Perform hyperparameter tuning and cross-validation to optimize models.
• Deploy machine learning models into production environments.
• Integrate models with existing systems and applications, ensuring robustness and scalability.
• Monitor model performance and retrain models as needed to maintain accuracy.
• Troubleshoot and resolve issues related to data pipelines and model performance.
• Work closely with data engineers, software developers, and business stakeholders to understand requirements and deliver solutions.
• Communicate findings and insights to non-technical stakeholders effectively.
• Stay updated with the latest research and advancements in data science and machine learning.
• Experiment with new techniques and tools to improve existing processes and models.
• Publish findings in top-tier data science or machine learning conferences and journals.

What makes you a great fit :

• Proven track record of leading and delivering successful data science projects.
• Experience mentoring and guiding junior team members.
• Proficiency in programming languages such as Python, R, or JavaScript.
• Strong understanding of machine learning frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, and Keras.
• Experience with data processing tools like Pandas, NumPy, and SQL.
• Knowledge of big data technologies such as Hadoop, Spark, and distributed computing.
• Familiarity with cloud platforms like AWS, Google Cloud, or Azure.
• Proficiency in web development technologies (e.g., HTML, CSS, JavaScript, Node.js, React).
• Analytical Skills: Strong problem-solving skills and the ability to analyze complex datasets.
• Communication Skills: Excellent verbal and written communication skills, with the ability to convey technical concepts to non-technical stakeholders.