Data Analyst jobs in India

The incumbent will be responsible for deploying analytics algorithms and tools on chosen tech stack for efficient and effective delivery. Responsibilities include delivering insights and targeted action plans, address specific areas of risk and opportunity, work cross-functionally with business and technology teams, and leverage the support of global teams for analysis and data

- This role focuses on supporting strategic HR projects with an emphasis on compensation, people analytics and talent management.

- It involves analyzing HR data, conducting benchmarking and driving initiatives like org design, job evaluation and workforce planning to support business goals.

You will have a key role in advancing how GIA utilises available data for its risk assessment and assurance planning, aiming to integrate this into a real-time data-led ecosystem. Your responsibilities include driving innovation and increasing the productivity of the audit function through the use of data, developing predictive models and use of Data Science techniques in the department.

The role of Credit Analyst involves assessing financial data to determine creditworthiness and providing insights to guide business decisions. This position is ideal for individuals with a keen eye for detail and expertise in financial analysis within the technology and telecoms industry.

Qualifications

 Bachelor's degree in computer engineering, Computer Science, Data Analytics, or related field.

 3-4 years of experience in BI engineering or data analytics and advanced SQL Programming like PL/SQL.  Strong hands-on experience with Microsoft Power BI (including Power Query, DAX, dataflows, etc.).  In-depth knowledge of report design, development and maintenance, data extraction from DWH and cubes layer and dimensional data modeling concepts.  Strong hands-on experience with Python/Spark scripting and shell (Unix/Linux) scripting.  Familiarity with data warehousing concepts and cloud data platforms (Azure, Snowflake, etc.) and their services like Azure Synapse Analytics, Azure Analysis Services, Azure Databricks, Azure SQL DW/Synapse.  Excellent communication and stakeholder management skills with Strong analytical abilities.  Self-starter and motivated with ability to work in a fast-paced development environment.  Proficiency in the development environment, including IDE, database server, GIT, Continuous Integration, unit-testing tool, and defect management tools.Responsibilities:

* Design, develop, and deploy Power BI dashboards, reports, and data models. * Support and monitor the existing reports in maintenance with clear documentation. * Optimize DAX queries and Power BI performance for scalability and responsiveness. * Automate the existing reports by removing manual steps and adhere reporting best practices. * Supports in analyzing reports, admin activities, usage monitoring, and licensing costs optimization. * Write custom scripts to extract data from unstructured/semi-structured sources from data warehouse or BI Cubes or from Snowflake. * Skilled in dashboard deployment, report consolidation, and enhancing report usability for end users. * Work with data engineers to define and validate data from the data sources for Power BI consumption. * Collaborate with business stakeholders to define KPIs and build self-service BI tools. * Proactive and clear communication about the report status with the stakeholders. * Provide Power BI training, support, and up to date documentation for business users and conduct KT sessions for the junior resources to guide and mentor themPreferred Skills:

* Microsoft Certified: Data Analyst Associate or equivalent Power BI certification. * Hands-on experience with Microsoft Power BI (including Power Query, DAX, dataflows, etc.). * Basic understanding of reporting governance and Power BI administration. * Working Knowledge of Dev-Ops processes (CI/CD), Azure DevOps, Git/Jenkins version control tool, reporting and maintenance of BI reports and SQL scripting. * Hands-on experience in extracting data from databases like (Azure SQL DB, MySQL/, Cosmos DB etc.), SharePoint, Python/Unix shell Scripting. Technologies we use: Power BI, Databricks, Python, PySpark, Scripting (Power shell, Bash), Azure SQL DW/Synapse, Azure Tabular/Cubes, Azure DevOps, Git, Terraform, Snowflake

Position:

ML EngineerJob type:

Techno-FunctionalPreferred education qualifications:

Bachelor/ Master's degree in computer science, Data Science, Machine Learning OR related technical degreeJob location:

IndiaGeography:

SAPMENARequired experience:

6-8 YearsPreferred profile/ skills:

• 5+ years in developing and deploying enterprise-scale ML solutions
• [Mandatory] Proven track record in data analysis (EDA, profiling, sampling), data engineering (wrangling, storage, pipelines, orchestration),
• [Mandatory] Proficiency in Data Science/ML algorithms such as regression, classification, clustering, decision trees, random forest, gradient boosting, recommendation, dimensionality reduction
• [Mandatory] Experience in ML algorithms such as ARIMA, Prophet, Random Forests, and Gradient Boosting algorithms (XGBoost, LightGBM, CatBoost)
• [Mandatory] Prior experience on MLOps with Kubeflow or TFX
• [Mandatory] Experience in model explainability with Shapley plot and data drift detection metrics.
• [Mandatory] Advanced programming skills with Python and SQL
• [Mandatory] Prior experience on building scalable ML pipelines & deploying ML models on Google Cloud
• [Mandatory] Proven expertise in ML pipeline optimization and monitoring the model's performance over time
• [Mandatory] Proficiency in version control systems such as GitHub
• Experience with feature engineering optimization and ML model fine tuning is preferred
• Google Cloud Machine Learning certifications will be a big plus
• Experience in Beauty or Retail/FMCG industry is preferred
• Experience in training with large volume of data (>100 GB)
• Experience in delivering AI-ML projects using Agile methodologies is preferred
• Proven ability to effectively communicate technical concepts and results to technical & business audiences in a comprehensive manner
• Proven ability to work proactively and independently to address product requirements and design optimal solutions
• Fluency in English, strong communication and organizational capabilities; and ability to work in a matrix/ multidisciplinary team

Job objectives:

Design, develop, deploy, and maintain data science and machine learning solutions to meet enterprise goals. Collaborate with product managers, data scientists & analysts to identify innovative & optimal machine learning solutions that leverage data to meet business goals. Contribute to development, rollout and onboarding of data scientists and ML use-cases to enterprise wide MLOps framework. Scale the proven ML use-cases across the SAPMENA region. Be responsible for optimal ML costs.Job description:

• Deep understanding of business/functional needs, problem statements and objectives/success criteria
• Collaborate with internal and external stakeholders including business, data scientists, project and partners teams in translating business and functional needs into ML problem statements and specific deliverables
• Develop best-fit end-to-end ML solutions including but not limited to algorithms, models, pipelines, training, inference, testing, performance tuning, deployments
• Review MVP implementations, provide recommendations and ensure ML best practices and guidelines are followed
• Act as 'Owner' of end-to-end machine learning systems and their scaling
• Translate machine learning algorithms into production-level code with distributed training, custom containers and optimal model serving
• Industrialize end-to-end MLOps life cycle management activities including model registry, pipelines, experiments, feature store, CI-CD-CT-CE with Kubeflow/TFX
• Accountable for creating, monitoring drifts leveraging continuous evaluation tools and optimizing performance and overall costs
• Evaluate, establish guidelines, and lead transformation with emerging technologies and practices for Data Science, ML, MLOps, Data Ops