Data Scientist
3 days ago
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)
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