<h3>Key Responsibilities (Functional Requirements):</h3><ul><li>Work closely with business stakeholders to understand, translate, and solve business problems using data-driven solutions.</li><li>Lead analytical initiatives, mentor junior analysts, and ensure timely, high-quality model delivery.</li><li>Translate business goals into measurable insights using ML and statistical techniques.</li><li>Communicate complex technical results effectively to both technical and non-technical stakeholders.</li><li>Collaborate in cross-functional teams to deliver impactful results and ensure stakeholder satisfaction.</li></ul><h3>Required Technical Expertise:</h3><ul><li>Strong hands-on experience with:Machine Learning: Regression, Classification, Time Series Forecasting, ClusteringAlgorithms: Random Forest, XGBoost, Decision Trees, SVM, KNN, Logistic/Linear Regression</li><li>Experience with deep learning methods such as LSTM and Neural Networks is a plus.</li><li>Proficiency in Python and PySpark for scalable data processing and modeling.</li><li>Experience in SQL, Excel, and working with large datasets.</li><li>Ability to design and implement robust, end-to-end ML workflows.</li></ul><h3>Bonus Skills (Good to Have):</h3><ul><li>Experience with Azure Databricks, MLOps, or optimization techniques</li><li>Familiarity with CI/CD and productionizing ML models</li></ul>
Key Responsibilities (Functional Requirements):
Work closely with business stakeholders to understand, translate, and solve business problems using data-driven solutions.
Lead analytical initiatives, mentor junior analysts, and ensure timely, high-quality model delivery.
Translate business goals into measurable insights using ML and statistical techniques.
Communicate complex technical results effectively to both technical and non-technical stakeholders.
Collaborate in cross-functional teams to deliver impactful results and ensure stakeholder satisfaction.
Required Technical Expertise:
Strong hands-on experience with:Machine Learning: Regression, Classification, Time Series Forecasting, ClusteringAlgorithms: Random Forest, XGBoost, Decision Trees, SVM, KNN, Logistic/Linear Regression
Experience with deep learning methods such as LSTM and Neural Networks is a plus.
Proficiency in Python and PySpark for scalable data processing and modeling.
Experience in SQL, Excel, and working with large datasets.
Ability to design and implement robust, end-to-end ML workflows.
Bonus Skills (Good to Have):
Experience with Azure Databricks, MLOps, or optimization techniques
Familiarity with CI/CD and productionizing ML models