Senior Data Scientist

Chennai, Bangalore, Hyderabad

Job Description

Job Title : Senior Data Scientist

Location : Chennai, Bangalore, Hyderabad

Who we are

Tiger Analytics is a global leader in AI and analytics, helping Fortune 1000 companies solve their toughest challenges. We offer full-stack AI and analytics services & solutions to empower businesses to achieve real outcomes and value at scale. We are on a mission to push the boundaries of what AI and analytics can do to help enterprises navigate uncertainty and move forward decisively. Our purpose is to provide certainty to shape a better tomorrow.

Our team of 4000+ technologists and consultants are based in the US, Canada, the UK, India, Singapore and Australia, working closely with clients across CPG, Retail, Insurance, BFS, Manufacturing, Life Sciences, and Healthcare. Many of our team leaders rank in Top 10 and 40 Under 40 lists, exemplifying our dedication to innovation and excellence.

We are a Great Place to Work-Certified™ (2022-24), recognized by analyst firms such as Forrester, Gartner, HFS, Everest, ISG and others. We have been ranked among the ‘Best’ and ‘Fastest Growing’ analytics firms lists by Inc., Financial Times, Economic Times and Analytics India Magazine

Role Overview:

We are seeking a seasoned Senior Data Scientist to join our dynamic team. This role offers the opportunity to lead end-to-end data science initiatives, from data engineering and predictive modeling to advanced analytics and machine learning model deployment. The ideal candidate will possess a strong foundation in Python, PySpark, Databricks, and Power BI, coupled with experience in predictive modeling, forecasting, and optimization techniques.

Key Responsibilities

1. ETL Development

Pipeline Development: Design, develop, and optimize scalable ETL pipelines using Python, PySpark, and Databricks to process large datasets efficiently.
Data Integration: Integrate data from various sources, including APIs, RDBMS, flat files, and streaming data, ensuring seamless data flow across systems.
Data Quality Assurance: Implement data quality checks, validations, and logging mechanisms to ensure the integrity and reliability of data pipelines.
Cloud Platform Utilization: Leverage cloud platforms (AWS, Azure, or GCP) and storage solutions to build and maintain robust data infrastructure.
Orchestration & Automation: Utilize orchestration tools like Databricks Workflows, Airflow, or Azure Data Factory to schedule and monitor pipelines, ensuring timely data availability.

2. Machine Learning & Advanced Analytics

Predictive Modeling: Build and deploy machine learning models for regression and classification tasks using algorithms such as Random Forest, XGBoost, and support vector machines.
Dimensionality Reduction: Apply techniques like PCA and t-SNE to reduce data complexity and enhance model performance.
Anomaly Detection: Implement anomaly detection models to identify outliers and unusual patterns in data.
Forecasting: Develop forecasting models using time series analysis and other statistical techniques to predict future trends.
Optimization: Utilize optimization methods, including Linear Programming and Goal Seek, to solve complex business problems and improve decision-making.

3. Reporting & Visualization

Power BI Dashboards: Develop and maintain interactive Power BI dashboards and reports to provide actionable insights to stakeholders.
Data Standardization: Standardize and harmonize data across different sources to ensure consistency and accuracy in reporting.
Stakeholder Collaboration: Work closely with business teams to understand reporting requirements and deliver solutions that meet business needs.

4. Automation & Process Improvement

Automation Scripting: Write Python scripts to automate repetitive tasks, improving efficiency and reducing manual intervention.
Process Optimization: Continuously evaluate and improve data processing workflows to enhance performance and scalability.
CI/CD Implementation: Implement Continuous Integration and Continuous Deployment practices to streamline development and deployment processes.

5. Stakeholder Management & Collaboration

Cross-functional Collaboration: Work closely with data scientists, analysts, and business stakeholders to understand requirements and deliver data solutions that drive business value.
Technical Leadership: Provide guidance and mentorship to junior team members, fostering a collaborative and high-performance team environment.
Communication: Effectively communicate technical concepts and insights to non-technical stakeholders, ensuring alignment with business objectives.

You are important to us, let’s stay connected!

Every individual comes with a different set of skills and qualities so even if you don’t tick all the boxes for the role today, we urge you to apply as there might be a suitable/unique role for you tomorrow. We are an equal opportunity employer. Our diverse and inclusive culture and values guide us to listen, trust, respect, and encourage people to grow the way they desire.