Job Description
- JOB SUMMARYDesigning and maintaining dataJOB 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 (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
- 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.
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