Senior Data Scientist

Role of Data scientist:

Undertaking data collection, preprocessing and analysis

Building models to address business problems

Presenting information using data visualization techniques

Required Skillsets

• Strong proficiency in Python and SQL for data extraction, cleaning, analysis, automation, and model development.

• Hands-on experience with pandas, NumPy, scikit-learn, and data visualization libraries.

• Good understanding of machine learning techniques such as regression, classification, clustering, decision trees, random forests, XGBoost/LightGBM, and model evaluation.

• Experience working with large and complex datasets from multiple sources.

• Strong working knowledge of AWS data and analytics services, including S3, Athena, Glue, Redshift, RDS, Lambda, IAM, CloudWatch, and EventBridge.

• Experience building or supporting ETL/data pipelines using AWS Glue, Lambda, scheduled jobs, APIs, and cloud storage.

• Familiarity with AWS SageMaker for model training, deployment, experiment tracking, and model monitoring will be preferred.

• Experience in feature engineering, data preprocessing, model validation, and performance tracking.

• Working knowledge of business analytics, funnel analysis, cohort analysis, customer segmentation, campaign performance measurement, and lead scoring.

• Experience with dashboards and reporting tools such as Power BI, Tableau, Streamlit, Shiny, or similar.

• Basic understanding of APIs, webhooks, data quality checks, logging, and production monitoring.

• Ability to translate business problems into analytical, machine learning, or automation solutions.

• Strong communication skills with the ability to explain insights clearly to business and leadership teams.

• Exposure to financial services, lending, marketing analytics, credit risk, or customer analytics will be an added advantage.

• Familiarity with GenAI/LLM use cases, prompt engineering, RAG, or chatbot analytics will be a plus.

Required Skillsets

• Strong proficiency in Python and SQL for data extraction, cleaning, analysis, automation, and model development.

• Good understanding of machine learning techniques such as regression, classification, clustering, decision trees, random forests, XGBoost/LightGBM, and model evaluation.

• Working Knowledge of building or supporting ETL/data pipelines using AWS Glue, Lambda, scheduled jobs, APIs, and cloud storage.

• Familiarity with AWS SageMaker for model training, deployment, experiment tracking, and model monitoring will be preferred.

• Experience in feature engineering, data preprocessing, model validation, and performance tracking.

• Basic understanding of APIs, webhooks, data quality checks, logging, and production monitoring.

• Ability to translate business problems into analytical, machine learning, or automation solutions.

• Strong communication skills with the ability to explain insights clearly to business and leadership teams.

• Exposure to financial services, lending, marketing analytics, credit risk, or customer analytics will be an added advantage.

• Familiarity with GenAI/LLM use cases, prompt engineering, RAG, or chatbot analytics will be a plus.

What do you need to succeed?

Degree in Engineering/ Computer Science/ Economics, Statistics or Mathematics

Data-driven mindset, critical thinking and problem-solving skill

4+ years of experience in analyzing large, multi-dimensional data sets

Expertise in converting insights into actionable solutions

Expertise in SQL, Excel, and visualization tools such as Power bi, Tableau

Experience with a statistical programming language like Python preferred

Machine learning experience of 4+years of implementing and successfully deploying ML solutions at scale for real-world problems.

Strong written and verbal communication skills to influence stakeholders