Data Scientist

Overview:

Key Responsibilities:

• Develop and implement machine learning models for predictive and prescriptive analysis.
• Collect, clean, and preprocess data for analysis and modeling purposes.
• Conduct exploratory data analysis to discover trends and patterns in data.
• Create and maintain data pipelines and infrastructures for efficient data retrieval and storage.
• Collaborate with cross-functional teams to identify business needs and opportunities for leveraging data to drive business solutions.
• Design and execute A/B tests to evaluate the effectiveness of different strategies and solutions.
• Develop and deploy data-driven solutions and algorithms to address business challenges.
• Present findings and insights to stakeholders using data visualization techniques.
• Stay updated with the latest advancements in data science and machine learning technologies.
• Contribute to the development and implementation of data governance and data quality standards.
• Conduct ad-hoc analysis and provide data-driven insights to support strategic decision-making.
• Collaborate with data engineers to optimize data infrastructure and improve data accessibility.
• Lead and mentor junior data analysts and data scientists.
• Participate in the design and development of data science projects and initiatives.
• Contribute to the creation and maintenance of best practices in data science and analytics.

Required Qualifications:

• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or related field.
• Proven experience in developing and deploying machine learning models and algorithms.
• Proficiency in programming languages such as Python, R, or Java.
• Solid understanding of statistical analysis, hypothesis testing, and experimental design.
• Experience with big data technologies and platforms such as Hadoop, Spark, or MapReduce.
• Expertise in data visualization tools and techniques (e.g., Tableau, Power BI, Matplotlib).
• Demonstrated knowledge of database management and query languages, including SQL and NoSQL databases.
• Strong communication and presentation skills with the ability to convey complex findings to non-technical stakeholders.
• Experience in designing and implementing data pipelines and ETL processes.
• Ability to work in a fast-paced, collaborative environment and manage multiple projects simultaneously.
• Solid understanding of cloud computing platforms, such as AWS, Azure, or Google Cloud.
• Knowledge of advanced analytics, including time series analysis, clustering, and classification techniques.
• Experience with data mining, feature engineering, and data preprocessing techniques.
• Strong problem-solving skills and the ability to think critically and analytically.
• Proven track record of delivering actionable insights and recommendations based on data analysis.