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.