About the role
Primary Skills
- Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio
Job requirements
- Experience Range: 6 to 8 years of experience developing modern software applications, including at least 5 years building and optimizing machine learning pipelines with Java, Python, or Scala, and 4 years working with distributed computing frameworks such as Spark, DASK, or Ray. Key Responsibilities: 1. Design, develop, and maintain scalable machine learning pipelines using Java, Python, or Scala to support advanced data science solutions. 2. Implement, optimize, and deploy distributed computing solutions leveraging frameworks such as Spark, DASK, or Ray to handle large-scale data processing and model training. 3. Lead the end-to-end delivery of low-latency inferencing solutions, ensuring robust performance and reliability in production environments. 4. Build and enhance deep learning pipelines for forecasting and personalization using frameworks such as PyTorch and TensorFlow. 5. Collaborate with cross-functional agile teams to design and implement RESTful web services and integrate machine learning components into broader service-oriented architectures. 6. Utilize real-time streaming technologies like Kafka or Storm to enable real-time data ingestion and processing for machine learning applications. 7. Drive experimentation, model evaluation, and statistical analysis using advanced techniques such as hypothesis testing, regression, and probabilistic graphical models. 8. Ensure quality and reproducibility of machine learning workflows by leveraging tools such as Git, JIRA, and containerization platforms like Docker or PCF. Required Skills: 1. Advanced proficiency in Java, Python, or Scala for software and ML pipeline development 2. Hands-on experience with distributed computing frameworks such as Apache Spark, DASK, or Ray 3. Deep understanding of machine learning frameworks including TensorFlow, PyTorch, Sci-Kit Learn, Keras, and MXNet 4. Expertise in statistical analysis and computing, including hypothesis testing, T-Test, Z-Test, and regression methods 5. Experience with real-time data streaming tools such as Kafka or Storm 6. Knowledge of building and deploying deep learning models for forecasting and personalization 7. Proficiency in RESTful web services and service-oriented architecture design 8. Experience with containerization technologies such as Docker or PCF 9. Familiarity with ML workflow orchestration tools like KubeFlow or BentoML 10. Proficiency with Git for version control Preferred Skills: 1. Experience with Great Expectations or Evidently AI for model monitoring and validation 2. Hands-on expertise in SAS, SPSS, or R/R Studio for advanced statistical modeling 3. Knowledge of advanced forecasting techniques including Exponential Smoothing, ARIMA, and ARIMAX 4. Familiarity with classification algorithms such as Decision Trees and SVM 5. Understanding of distance metrics like Hamming, Euclidean, and Manhattan Distance 6. Experience in container orchestration and deployment within cloud or hybrid environments 7. Experience using agile project management tools such as JIRA Desired Qualifications: 1. Bachelor's degree in Computer Science, Information Systems, Data Science, or a closely related field 2. Relevant certification in machine learning, data engineering, or distributed computing (such as TensorFlow Developer Certificate or Databricks Certified Associate Developer for Apache Spark) Additional Information: This role requires working from the office at Manyata Tech Park, Bangalore, for at least 4 days per week.