Data Analyst Job Openings At Phenom – Apply Now!

Qualifications

Education and Qualifications:

• Bachelor’s degree in computer science, Information Technology, or related field

Experience:

• Looking for “3+ years” of experience as a Data Analyst
• Should be proficient in writing complex SQL queries
• Preferably looking for someone from Hyderabad

Desired/Good To Have Skills:

• Cloud platforms: Familiarity with cloud platforms for data analysis and orchestration like Snowflake, AWS Sagemaker, AWS Lambda, Airflow, Jenkins, Postman
• Machine Learning: Proficiency with fundamental machine learning algorithms such as linear regression, logistic regression, decision trees, random forests, and neural networks. Ability to use ML and statistical modeling to solve business problems.
• Feature Selection and Dimensionality Reduction: Familiarity with techniques like feature selection, feature engineering, regularization, and dimensionality reduction (e.g., PCA) to improve model efficiency, reduce overfitting, and enhance interpretability.
• Model Deployment: Experience in deploying machine learning models in production environments, utilizing frameworks like Flask or Django. Understanding of model serving, API development, and cloud deployment to enable real-time predictions.
• Dashboarding: Familiarity with BI and dashboarding software such as Superset, Looker and Tableau.
• Candidates with experience in the credit card, banking, or healthcare domains and/or strong mathematical and statistical backgrounds will be preferred.

What You’ve Done:

• Data Manipulation: Efficiently manipulate and analyze large datasets. Strong experience in extracting and reporting data using complex queries.
• Statistical Analysis: Proficiency with statistical concepts and techniques, including exploratory data analysis, hypothesis testing, confidence intervals, and experiment design. Proficiency in using statistical methods to derive meaningful conclusions from data.
• Data Visualization: Proficiency in using visualization libraries like Matplotlib or Seaborn to create clear, concise, and intuitive visualizations that communicate complex data insights to stakeholders.
• Model Evaluation and Validation: Proficiency in assessing model performance using metrics such as accuracy, precision, recall, F1 score, and area under the curve (AUC). Proficiency in techniques like cross-validation, train-test splits, and hyperparameter tuning to ensure robust model performance.
• Communication: Strong written, verbal, and visual communication skills.