Data Scientist
New
S
SupersubFintech
IndiaFull-TimeMiddle
Salary3,000 - 4,000 USD per year
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Job Details
- Experience
- 5+ years of experience in data science, analytics, or related fields.
- Required Skills
- PythonSQLMachine LearningMicrosoft Power BIAirflowR
Requirements
- Bachelor’s or Master’s degree in Data Science, Computer Science, Mathematics, Statistics, or a related field.
- 5+ years of experience in data science, analytics, or related fields.
- Strong SQL skills and experience with relational and NoSQL databases.
- Proficiency in Python, including Pandas, NumPy, and Scikit-Learn, or R for data analysis and machine learning.
- Hands-on experience with ETL pipelines, data processing, and data warehousing.
- Experience with data warehouses such as Snowflake, Redshift, or BigQuery.
- Knowledge of OCI, GCP, or Azure and experience with tools such as Airflow, DBT, Spark, or Kafka.
- Experience with BI tools such as Power BI, Tableau, Looker, or Metabase.
- Strong understanding of statistics, machine learning algorithms, and predictive modeling.
- Experience in fintech, banking, or finance is a plus.
- Familiarity with big data technologies such as Hadoop, Spark, or Databricks is a plus.
- Knowledge of data governance, compliance, and security best practices is a plus.
- Experience with real-time analytics and streaming data is a plus.
Responsibilities
- Design, build, and maintain ETL/ELT pipelines for structured and unstructured data.
- Develop and manage data warehouse architecture.
- Integrate and optimize data from databases, APIs, third-party tools, and business applications.
- Ensure data integrity, consistency, and security across systems.
- Collaborate with business teams to develop KPI dashboards and reports.
- Analyze and visualize data using SQL, Python, R, or BI tools.
- Apply machine learning and statistical modeling to identify trends and predict outcomes.
- Implement A/B testing frameworks and experiments to measure business impact.
- Optimize algorithms for fraud detection, customer segmentation, demand forecasting, and operational efficiency.
- Work with engineers and business stakeholders to align analytics solutions with business needs.
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