Hybrid Senior Data Analyst

Data Science And Machine Learning Engineer

Designing and maintaining data

Job Description

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Statistics, Mathematics, or related field.
• Strong experience in machine learning algorithms, predictive modeling, and data mining.
• Proficiency in Pyspark, Python pandas for data science workloads.
• Strong SQL knowledge and experience with relational databases.
• Minimum 3 years of experience with data visualization tools such as Power BI, Dax Queries, and best practices.
• Experience with Azure Databricks, Google Cloud, and modern data science libraries (e.g., scikit-learn, pandas, NumPy).
• Experience with GenAI and large language models.
• Ability to interpret complex datasets and produce actionable insights.
• Must know how to analyze the root cause of dashboard errors.
• Have experience in ML Ops and have strong coding background.
• Have experience with Natural Language Processing (NLP).
• Knowledge or experience with A/B Testing.
• Working knowledge of designing, training, and implementing machine learning models.
• Familiarity with cloud-based infrastructure.
• Excellent communication and problem-solving skills.
• 7 or more years of experience in data science and machine learning engineering.

Additional Skills (Skills that are a plus, but not required)

• Knowledge of statistical methods and experimental design.

Responsibilities

• Key Responsibilities

  Advanced Analytics & Machine Learning

  • Design, develop, and optimize machine learning models (forecasting, classification, clustering).
  • Apply data mining techniques to uncover patterns and insights in large datasets.
  • Perform feature engineering, model validation, and performance tuning.
  • Explore and deploy modern AI and ML approaches to enhance automation and analytics.

  Data Preparation & Quality

  • Prepare structured and unstructured data for modeling and advanced analysis.
  • Develop scripts and tools for data cleansing, validation, and enrichment.
  • Collaborate with Data Engineering to maintain efficient data pipelines.
  • Identify data quality issues and propose remediation.

  Analytics, Insights & Reporting

  • Conduct deep-dive analyses to identify trends and improvement opportunities.
  • Communicate complex findings in clear, concise ways to technical and non-technical stakeholders.
  • Support the development of dashboards, metrics, and analytical solutions.

  Cross-Team Collaboration

  • Work with architects, engineers, and analysts to define analytical requirements.
  • Contribute to conceptual data model design and workflow optimization.
  • Promote best practices in machine learning, analytics, and data governance.