Data Science Analyst

Job Summary:

The Data Scientist will be responsible for analyzing complex datasets to extract valuable insights, build predictive models, and support data-driven decision-making. This role involves collaborating with business, engineering, and product teams to identify opportunities where data science can drive measurable business outcomes.

Key Responsibilities:

• Collect, clean, and analyze large structured and unstructured datasets from multiple sources.
  
• Develop and implement

  machine learning (ML)

  and

  statistical models

  to solve real-world business problems.
• Work with stakeholders to define business objectives and translate them into analytical solutions.
  
• Design and execute

  A/B tests

  , data experiments, and hypothesis testing.
• Visualize data and communicate results effectively using dashboards, reports, and presentations.
  
• Collaborate with data engineers to optimize data pipelines and ensure data quality.
  
• Stay current with emerging technologies in

  AI, ML, and data analytics

  .
• Document methodologies, model performance, and analytical processes.
  
• Ensure data privacy, governance, and compliance standards are met.
  

Required Skills and Qualifications:

• Bachelors or Master’s degree in

  Computer Science, Statistics, Mathematics, Data Science, or a related field

  .
• 3+ years of hands-on experience in

  data analysis, machine learning, or predictive modeling

  .
• Proficiency in

  Python or R

  , and experience with data science libraries (NumPy, pandas, scikit-learn, TensorFlow, PyTorch, etc.).
• Strong knowledge of

  SQL

  and experience working with relational and NoSQL databases.
• Experience with

  data visualization tools

  such as Power BI, Tableau, or Plotly.
• Solid understanding of

  statistics, probability, and experimental design

  .
• Ability to communicate technical findings clearly to non-technical audiences.
  

Preferred Qualifications:

• Experience with

  big data technologies

  (Spark, Hadoop).
• Knowledge of

  cloud platforms

  (AWS, Azure, GCP) and data tools (Databricks, Snowflake, BigQuery).
• Experience deploying models to production (MLOps tools like MLflow, Airflow, or Kubeflow).
  
• Familiarity with

  natural language processing (NLP)

  or

  computer vision

  techniques.
• Publications, Kaggle participation, or open-source contributions are a plus.
  

Key Performance Indicators (KPIs):

• Accuracy and business impact of predictive models.
  
• Reduction in decision-making time through data insights.
  
• Adoption rate of data-driven recommendations.
  
• ROI or efficiency gains from implemented analytics solutions