Sr Advanced Software Engr

Responsibilities

• Minimum of 10 years’ experience - Lead moderately to highly complex data science and analytics projects, applying a diverse range of data science, statistical, and machine learning techniques.
• Own the end-to-end development lifecycle of data science and analytics solutions, including problem analysis, stakeholder engagement, solution design, integration, deployment, maintenance, and support.
• Independently design, develop, test, and maintain data science models and algorithms, following disciplined software development and model-development practices.
• Evaluate emerging analytical and technical capabilities and recommend solutions that improve productivity and quality, reduce cycle time, and enhance operational reliability.
• Develop and promote guidelines, standards, and best practices for analytics and machine learning models, including model development, validation, deployment, monitoring, and lifecycle management.
• Collaborate with engineering, product, and business stakeholders to translate complex requirements into scalable, measurable data science solutions.

You Must Have

• Minimum 2 years of relevant experience in data science, machine learning, analytics, or related technical disciplines.
• Strong analytical and problem-solving skills, proficiency with software applications, and the ability to produce and interpret technical documentation.
• Working knowledge of statistics, statistical modeling, and statistical process control.
• Hands-on experience with scripting and query languages such as Python, R, and SQL.
• Experience developing analytics dashboards and data visualizations to communicate insights effectively.
• Practical experience applying machine learning, neural networks, and deep learning techniques.
• Ability to translate analytical findings into clear recommendations for both technical and non-technical stakeholders.

Good to Have

• Experience taking machine learning models from development through production deployment and monitoring.
• Familiarity with ML frameworks such as scikit-learn, TensorFlow, or PyTorch.
• Experience with cloud data/ML platforms such as Azure Machine Learning or Databricks.
• Understanding of MLOps, including experiment tracking, model versioning, monitoring, and reproducibility.
• Exposure to NLP, Generative AI, LLMs, or RAG-based applications.
• Experience working with large or complex datasets and distributed processing technologies such as Apache Spark.