Data Science Specialist

Key Responsibilities:

• Analyze and interpret large, complex datasets using statistical and machine learning techniques.
• Develop predictive models and machine learning solutions using frameworks like scikit-learn, TensorFlow, or PyTorch.
• Perform statistical analysis including hypothesis testing, regression modeling, and A/B testing.
• Clean, manipulate, and prepare data using Python libraries such as Pandas, NumPy.
• Visualize insights and trends using Matplotlib, Seaborn, or ggplot2 to support storytelling and decision-making.
• Write efficient queries using SQL to extract and manipulate data from relational databases.
• Collaborate with business stakeholders to understand requirements, identify data-driven opportunities, and present findings.
• Work with big data tools (e.g., Spark, Hive, Hadoop) to manage and process large-scale datasets.
• Deploy models and analytics pipelines to the cloud (AWS, Azure, GCP) and support their ongoing performance.
• Use version control (Git) and documentation best practices to ensure reproducibility and collaboration.

Required Qualifications:

• Proficiency in Python for data analysis, modeling, and scripting.
• Strong knowledge of statistics and probability, including experience with hypothesis testing, regression, and inferential statistics.
• Hands-on experience with machine learning algorithms (classification, regression, clustering, ensemble methods).
• Experience with data science tools such as Pandas, NumPy, Scikit-learn.
• Strong data visualization skills using tools like Matplotlib, Seaborn, or ggplot2.
• Proficient in SQL and understanding of relational databases.
• Familiarity with big data frameworks (Spark, Hive, or Hadoop).
• Experience working on cloud platforms such as AWS, Azure, or GCP.
• Understanding of data preprocessing techniques (cleaning, feature engineering, dimensionality reduction).
• Experience with version control systems like Git.

Preferred Qualifications:

• Master’s or PhD in Data Science, Statistics, Computer Science, Mathematics, or a related field.
• Experience with deep learning and neural networks.
• Exposure to MLOps, model deployment, and monitoring tools.
• Domain knowledge in [Finance / Healthcare / E-commerce / etc.] (customize as per your industry).