Remote – Data scientist opportunity with Infometry Inc, Bengaluru

Job Overview:

The company seeks skilled professionals with experience in software/backend/data science, specializing in developing robust machine learning (ML) systems. This role involves working on end-to-end ML lifecycle projects in a collaborative, cross-functional environment. Candidates must have a strong software engineering background and proficiency in Python. The role also emphasizes leadership responsibilities like code reviews and technical guidance

Job Highlights:

Key Responsibilities:

• ML Model Development: Lead the design, development, and deployment of machine learning models tailored to meet organizational needs.
• Cross-functional Collaboration: Work with management, data engineering, and brand teams to build scalable ML pipelines.
• MLOps Implementation: Develop model serving pipelines and automate processes using MLOps frameworks like MLflow and SageMaker.
• Code Reviews & Collaboration: Review code, participate in problem-solving sessions and promote best practices within the team.
• Distributed Computing: Use frameworks like Snowpark and PySpark to scale data processing and model training efforts.
• Model Building: Develop a range of models, from simple linear/logistic regression to complex deep learning architectures.
• Training on Imbalanced Data: Implement strategies for model training on imbalanced datasets and perform model tuning.
• Model Performance Communication: Articulate model evaluation metrics to both technical and non-technical stakeholders.
• Model Monitoring: Diagnose model/data drift and implement time-wise performance tracking for reliable outcomes.

Required Skills and Qualifications:

• Programming: Strong Python skills with experience building scalable ML pipelines.
• Distributed Computing: Experience with Snowpark, PySpark, or similar frameworks.
• Software Engineering: Knowledge of software design, testing, and deployment best practices.
• End-to-End ML Lifecycle: Hands-on experience with all stages of the ML lifecycle, from data preprocessing to model deployment.
• Model Building: Proficiency in developing various ML models, including deep learning and regression models.
• Imbalanced Data Handling: Experience designing strategies to train models on imbalanced datasets.
• Model Evaluation: Familiarity with training schemas like cross-validation for unbiased model tuning.
• MLOps: Experience with frameworks like MLflow, SageMaker, and other pipeline automation tools.
• Collaboration: Strong problem-solving skills with the ability to contribute to team discussions.
• Tools: Familiarity with data science tools such as Dataiku, Databricks, and SageMaker.

This is an exciting opportunity for individuals who are passionate about integrating machine learning and software engineering in a dynamic, collaborative setting.

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