Sr ML and AI (Legacy)

As a Machine Learning Engineer, you will be responsible for designing and developing robust analytics models using statistical and machine learning algorithms. Working closely with product and engineering teams, your role involves solving complex business problems, identifying data-driven opportunities, and creating personalized customer experiences. Your main tasks will include building end-to-end machine learning solutions, implementing models in production, and utilizing various data frameworks and tools like Python, Spark, and Databricks. Your key responsibilities will revolve around analytics model development. This will involve analyzing use cases and designing appropriate analytics models tailored to specific business requirements. You will develop machine learning algorithms to drive personalized customer experiences, provide actionable business insights, and apply data mining techniques for tasks such as forecasting, prediction, segmentation, recommendation, and fraud detection. In addition, you will work on data engineering and preparation tasks. This includes extending company data with third-party data to enhance analytics capabilities, improving data collection procedures, and preparing raw data for analysis. You will be responsible for cleaning, imputing missing values, and standardizing data formats using Python data frameworks such as Pandas and NumPy. When it comes to machine learning model implementation, you will implement models considering both performance and scalability using tools like PySpark in Databricks. You will design and build infrastructure to support large-scale data analytics and experimentation, and utilize tools like Jupyter Notebooks for data exploration and model development. To qualify for this role, you should have an undergraduate or graduate degree in Computer Science, Mathematics, Physics, or related fields. While a PhD is preferred, it is not necessary. You should have at least 5 years of experience in data analytics, with a strong understanding of core statistical algorithms like classification and regression analysis. Proficiency in Python-based machine learning libraries, analytics platforms like Databricks, and tools such as Pandas, NumPy, and Jupyter Notebooks is essential. Additionally, you should have at least 4 years of continuous experience with Spark, particularly PySpark implementation.,