Sr Data Analyst

Job Summary:A Data Scientist is responsible for collecting, analyzing, and interpreting large datasets to uncover actionable insights and support data-driven decision-making. They collaborate with cross-functional teams, use various data analysis tools and techniques, and often have domain-specific expertise in industries such as healthcare, finance, marketing, or technology.

Key Responsibilities:

1. Data Collection and Cleaning:

   • Gather and clean large datasets from various sources, ensuring data accuracy and consistency.
   • Handle missing data and outliers effectively to prepare data for analysis.

2. Data Analysis and Modeling:

   • Apply statistical and machine learning techniques to analyze data and derive meaningful insights.
   • Develop predictive and prescriptive models to solve business problems and make recommendations.
   • Conduct hypothesis testing and A/B testing to validate findings.

3. Data Visualization:

   • Create visually appealing and informative data visualizations (charts, graphs, dashboards) to communicate insights to stakeholders.
   • Use tools like Matplotlib, Seaborn, Tableau, or Power BI for visualization.

4. Feature Engineering:

   • Identify and engineer relevant features from raw data to improve model performance.
   • Utilize domain knowledge to select the most informative features.

5. Machine Learning and AI Development:

   • Build and deploy machine learning models and algorithms for various applications, such as recommendation systems, fraud detection, or demand forecasting.
   • Optimize model performance and monitor them in production.

6. Data Interpretation and Communication:

   • Translate complex data findings into actionable recommendations for non-technical stakeholders.
   • Present results and insights through reports, presentations, or data storytelling.

7. Collaboration:

   • Collaborate with cross-functional teams, including engineers, product managers, and business analysts, to define data-driven solutions.
   • Act as a bridge between technical and non-technical teams.

8. Continuous Learning:

   • Stay updated with the latest advancements in data science, machine learning, and relevant technologies.
   • Participate in online courses, conferences, or workshops to enhance skills.

Qualifications:

• Bachelor's or Master's degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Physics, Engineering).
• Proficiency in programming languages such as Python or R.
• Strong knowledge of statistics and mathematics.
• Experience with data manipulation libraries (e.g., Pandas, NumPy) and machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
• Proficiency in SQL for data querying.
• Excellent problem-solving and critical-thinking skills.
• Effective communication skills to convey complex findings to non-technical stakeholders.
• Strong organizational skills and attention to detail.
• Experience with big data tools (e.g., Hadoop, Spark) and cloud platforms (e.g., AWS, Azure) may be a plus.