Data Scientist

Job Title: Data Scientist

Position Summary:

We are looking for a Data Scientist to help analyze our growing data, build data-driven solutions, and deliver actionable insights that will support key business decisions and innovations across teams.

Key Responsibilities:

- Collect, clean and preprocess structured/unstructured data from multiple sources

- Analyze large datasets to discover trends, patterns and insights that can drive strategy

- Build and train predictive / statistical / machine-learning models to solve business problems

- Design and implement data pipelines / ETL workflows where necessary

- Visualize and communicate findings via reports or dashboards to both technical and non-technical stakeholders

- Collaborate with product, engineering, and business teams to identify opportunities for data-driven features and solutions

- Monitor and maintain the performance, reliability, and integrity of data models (especially those in production)

- Stay updated with the latest developments in data science / analytics / ML and apply them when relevant

Required Qualifications:

- Bachelor’s or Master’s in Data Science, Computer Science, Statistics, Mathematics or related field

- Proficiency in Python (or R), data manipulation libraries, and SQL (or equivalent database/querying tools)

- Strong knowledge of statistics, machine learning / modeling techniques, and data analysis fundamentals

- Experience working with large and complex datasets; data cleaning and preprocessing experience

- Analytical mindset, problem-solving skills, attention to detail

- Good communication skills, ability to turn technical results into actionable business insights

- Ability to collaborate well with cross-functional teams

Preferred / Nice-to-have:

- Familiarity with data visualization tools (Tableau, Power BI, or equivalent) or Python/R visualization libraries

- Experience with Big Data tools (Spark/Hadoop) and handling large-scale data pipelines

- Exposure to cloud computing platforms (AWS, GCP, Azure) for data processing or storage

- Experience in deploying ML models / working in production environment

- Basic understanding of data engineering practices (ETL, data warehousing, pipelines)