Data Scientist - AI and Analytics

Desible.ai; Bengaluru; On-site; INR 900,000 - 1,300,000; Full time

Job summary

Desible.ai is seeking a Data Scientist with 2–3 years of experience in data analytics, statistics, and machine learning to design and implement analytical solutions. You will perform EDA, build predictive models, and prepare data for analytics while collaborating with data engineers and business teams to deliver scalable, cloud-based workflows.

The role emphasizes Python and SQL proficiency, experience with data visualization, and familiarity with cloud platforms.

Qualifications

• ED A, statistical analysis, and data visualization to identify trends and insights.
• Develop, train, evaluate, and optimize machine learning models for predictive analytics and business use cases.
• Clean, transform, and prepare structured and unstructured data for analytics and ML.
• Write Python scripts and SQL queries for data extraction, transformation, validation, and analysis.
• Develop and maintain basic ETL/ELT pipelines and automated data processing workflows.
• Work with databases, APIs, cloud storage, and data warehouses to access and integrate data from multiple sources.
• Support data modelling, data quality checks, and data validation processes.
• Work with cloud-based analytics and data platforms to process and analyse datasets.
• Collaborate with data engineering teams to improve data availability, reliability, and quality.
• Communicate analytical findings and recommendations through reports, dashboards, and visualizations.
• Support AI and Generative AI initiatives as required.

Responsibilities

• Perform exploratory data analysis (EDA), statistical analysis, and data visualization to identify trends and business insights.
• Develop, train, evaluate, and optimize machine learning models for predictive analytics and business use cases.
• Clean, transform, and prepare structured and unstructured data for analytics and machine learning.
• Write Python scripts and SQL queries for data extraction, transformation, validation, and analysis.
• Develop and maintain basic ETL/ELT pipelines and automated data processing workflows
• Work with databases, APIs, cloud storage, and data warehouses to access and integrate data from multiple sources.
• Support data modelling, data quality checks, and data validation processes.
• Work with cloud-based analytics and data platforms to process and analyse datasets.
• Collaborate with data engineering teams to improve data availability, reliability, and quality.
• Communicate analytical findings and recommendations through reports, dashboards, and visualizations.
• Support AI and Generative AI initiatives as required.

Skills

Python

SQL

Pandas

NumPy

scikit-learn

Statistics

Hypothesis testing

Regression

Machine learning

Data visualization

Matplotlib

Seaborn

Power BI

Tableau

Education

Bachelor's or Master’s degree in CS/DS/Statistics/Math/Engineering

Tools

Databricks

Apache Spark/PySpark

Azure Data Factory

Git

REST APIs

JSON

AWS

GCP

Job description

Job Title: Data Scientist - Analytics & AI

Experience: 2 - 3 Years

About the Role

We are looking for a Data Scientist with 2–3 years of experience in data analytics, statistical analysis, and machine learning, along with basic data engineering and cloud skills. The ideal candidate should be proficient in Python and SQL, capable of analysing complex datasets, developing predictive models, and building data workflows to support analytics and AI-driven business solutions.

The candidate will work closely with data engineers, business analysts, and technology teams to transform raw data into actionable insights and support the development of scalable, cloud-based analytical solutions.

Key Responsibilities

• Perform exploratory data analysis (EDA), statistical analysis, and data visualization to identify trends and business insights.
• Develop, train, evaluate, and optimize machine learning models for predictive analytics and business use cases.
• Clean, transform, and prepare structured and unstructured data for analytics and machine learning.
• Write Python scripts and SQL queries for data extraction, transformation, validation, and analysis.
• Develop and maintain basic ETL/ELT pipelines and automated data processing workflows
• Work with databases, APIs, cloud storage, and data warehouses to access and integrate data from multiple sources.
• Support data modelling, data quality checks, and data validation processes.
• Work with cloud-based analytics and data platforms to process and analyse datasets.
• Collaborate with data engineering teams to improve data availability, reliability, and quality.
• Communicate analytical findings and recommendations through reports, dashboards, and visualizations.
• Support AI and Generative AI initiatives as required.

Required Technical Skills

1. Data Science and Analytics

• Strong knowledge of Python and SQL.
• Experience with pandas, NumPy, and scikit-learn.
• Understanding of statistics, hypothesis testing, regression, and exploratory data analysis.
• Knowledge of supervised and unsupervised machine learning algorithms.
• Familiarity with data visualization tools such as Matplotlib, Seaborn, Power BI, or Tableau.

2. Basic Data Engineering

• Understanding of ETL/ELT processes and data pipeline development.
• Experience with SQL-based data extraction, joins, transformations, and aggregations.
• Familiarity with relational databases and data modelling concepts.
• Basic understanding of REST APIs, JSON, CSV, and other data formats.
• Exposure to data warehouses, data lakes, and cloud storage.
• Familiarity with Git and version control.

3. Cloud and Data Platforms

• Basic hands-on experience with at least one cloud platform: Azure, AWS, or GCP.
• Exposure to cloud-based data analytics and processing services.
• Familiarity with Databricks, Apache Spark/PySpark, Azure Data Factory, or equivalent technologies is an advantage.

4. AI and Machine Learning

• Understanding of machine learning workflows, feature engineering, and model evaluation.
• Basic knowledge of predictive analytics and model validation.
• Exposure to Generative AI, LLMs, or AI-powered applications is a plus.

5. Development and Deployment

• Basic understanding of CI/CD, automated testing, and workflow scheduling.
• Familiarity with APIs and model deployment concepts is desirable.

Qualifications and Experience

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
• 2–3 years of relevant experience in Data Science, Analytics, or Machine Learning.
• Strong analytical thinking and problem-solving skills.
• Ability to explain technical findings and business insights clearly.
• Willingness to work across analytics, data engineering, and AI use cases.