Sr. Data Analyst

Job Description -
Required Qualifications:

• Bachelors or Masters 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 (required) for data science workloads.
• Strong SQL (required) 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
  o Design develop and optimize machine learning models (forecasting classification clustering).
  o Apply data mining techniques to uncover patterns and insights in large datasets.
  o Perform feature engineering model validation and performance tuning.
  o Explore and deploy modern AI and ML approaches to enhance automation and analytics.
• Data Preparation & Quality
  o Prepare structured and unstructured data for modeling and advanced analysis.
  o Develop scripts and tools for data cleansing validation and enrichment.
  o Collaborate with Data Engineering to maintain efficient data pipelines.
  o Identify data quality issues and propose remediation.
• Analytics Insights & Reporting
  o Conduct deep-dive analyses to identify trends and improvement opportunities.
  o Communicate complex findings in clear concise ways to technical and non-technical stakeholders.
  o Support the development of dashboards metrics and analytical solutions.
• Cross-Team Collaboration
  o Work with architects engineers and analysts to define analytical requirements.
  o Contribute to conceptual data model design and workflow optimization.
  o Promote best practices in machine learning analytics and data governance.