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 .