Data Scientist

Peer Data is looking for a highly skilled and motivated Data Scientist to join our team, with a primary focus on ensuring data quality, driving automation, and improving processes across the organization.

This role requires a blend of technical expertise, problem-solving skills, and collaboration with cross-functional teams to deliver impactful solutions.

The ideal candidate will have a strong background in data analysis, process optimization, and automation, with the ability to translate business needs into actionable solutions.

Key Responsibilities: Data Quality Management: Develop and implement frameworks to monitor, measure, and improve data quality across the organization.

Identify and resolve data inconsistencies, inaccuracies, and gaps through advanced analytics and data cleansing techniques.

Collaborate with data operations and all associated key stakeholders to establish data quality standards and best practices.

Create automated data validation processes to ensure accuracy and reliability of data pipelines.

Automation and Process Improvement: Identify opportunities for automation in data workflows, reporting, and analytics processes.

Design and implement automated solutions to streamline repetitive tasks and improve efficiency.

Continuously evaluate and optimize existing processes to enhance productivity and reduce manual effort.

Leverage tools and technologies (e.g., Python, SQL, RPA, etc.) to build scalable automation solutions.

Strategic Capability Rollouts: Partner with product and development teams to support the implementation of new data-driven capabilities and tools.

Provide analytical expertise to ensure successful deployment of strategic initiatives.

Collaborate with business stakeholders to understand requirements and translate them into technical solutions.

Monitor and evaluate the impact of new capabilities, ensuring alignment with organizational goals.

Cross-Functional Collaboration: Work closely with data operations, product, technology and business teams to ensure seamless integration of data solutions.

Act as a trusted advisor to stakeholders, providing insights and recommendations based on data analysis.

Communicate complex technical concepts to non-technical audiences in a clear and concise manner.

Innovation and Continuous Learning: Stay up-to-date with industry trends and emerging technologies in data science, automation, and process improvement.

Experiment with new tools and techniques to drive innovation and improve existing processes.

Share knowledge and mentor team members to foster a culture of continuous improvement.

Qualifications: Education: Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Engineering, or a related field.

Experience: 2+ years of experience in data science, data analysis, or a related role.

Proven track record of improving data quality and implementing automation solutions.

Experience with process improvement methodologies (e.g., Lean, Six Sigma) is a plus.

Technical Skills: Proficiency in programming languages such as Python, R, or SQL.

Experience with data visualization tools (e.g., Tableau, Power BI).

Familiarity with automation tools (e.g., Alteryx, UiPath, or similar).

Strong understanding of data pipelines, ETL processes, and data governance.

Knowledge of machine learning and predictive modeling is a plus.

Soft Skills: Strong problem-solving and critical-thinking abilities.

Excellent communication and collaboration skills.

Ability to manage multiple projects and prioritize effectively.

Detail-oriented with a focus on delivering high-quality results.

Preferred Qualifications: Experience working in Agile or Scrum environments.

Familiarity with cloud platforms (e.g., AWS, Azure, GCP).

Knowledge of data governance frameworks and compliance standards.

Work Arrangement: This is a full-time, onsite role based in Bangalore, India .

Candidates must be willing to work from our office location.

The working hours for this role will be aligned with business needs, primarily supporting European or early US market hours .

Candidates should be prepared to work schedules that accommodate these time zones.

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