Data Scientist

Gather structured and unstructured data from multiple sources, including databases, APIs, and web scraping tools.
Preprocess and clean data to ensure accuracy, completeness, and consistency.
Handle missing data, remove duplicates, and standardize formats for analysis.
Conduct exploratory data analysis (EDA) to identify trends, patterns, and relationships in datasets.
Use visualization tools like Matplotlib, Seaborn, or Tableau to create insightful reports and dashboards.
Perform statistical analysis to validate hypotheses and business assumptions.
Develop machine learning models (e.g., regression, classification, clustering) using libraries like scikit-learn, TensorFlow, or PyTorch.
Fine-tune models through hyperparameter optimization and validation techniques.
Test and validate model accuracy using performance metrics (e.g., precision, recall, RMSE).
Collaborate with business stakeholders to understand their requirements and translate them into data-driven solutions.
Present actionable insights and recommendations to aid decision-making.
Define KPIs and success metrics for business objectives.
Create interactive dashboards and visualizations using tools like Power BI, Tableau, or D3.js.
Present complex data in a clear and concise manner to technical and non-technical audiences.
Automate reporting processes for regular updates.
Work closely with cross-functional teams, including software engineers, product managers, and business analysts.
Communicate findings effectively to stakeholders and provide strategic recommendations.
Document processes, workflows, and project results for knowledge sharing.
Collaborate with data engineers to design and maintain data pipelines and ETL workflows.
Integrate data from various sources into centralized repositories (e.g., data lakes or warehouses).
Ensure efficient data storage, retrieval, and management.
Keep up with the latest advancements in data science, AI, and ML technologies.
Participate in knowledge-sharing sessions, hackathons, and training programs.
Develop a strong understanding of the business domain (e.g., finance, healthcare, e-commerce) to align models and solutions effectively.
Tailor data science methodologies to industry-specific challenges and opportunities.