What are the responsibilities and job description for the Data Scientist position at Aptino?
Job Details
Job Title: Data Scientist
Job Summary
We are looking for a skilled Data Scientist who can extract meaningful insights from complex data, build predictive models, and drive data-informed decisions across the organization. The role involves collecting, processing, and analyzing large datasets, developing machine learning solutions, and communicating results to technical and non-technical stakeholders.
Key Responsibilities
Data Collection & Preparation
- Gather, clean, preprocess, and validate large structured and unstructured datasets from multiple sources.
- Ensure data quality, consistency, and accessibility for analysis.
Modeling & Analysis
- Develop, test, and deploy predictive models, machine learning algorithms, and statistical analyses to solve business problems.
- Perform exploratory data analysis to identify trends, patterns, and actionable insights.
Visualization & Reporting
- Create dashboards, reports, and visualizations to present results clearly and effectively to stakeholders.
- Translate complex findings into business recommendations and storytelling.
Collaboration
- Work closely with cross-functional teams (product, engineering, finance, marketing) to understand data needs and integrate analytical solutions.
- Communicate insights and model results to both technical and non-technical audiences.
Model Deployment & Monitoring
- Collaborate with data engineers or ML engineers to operationalize models and monitor their performance in production.
- Ensure data science solutions are scalable, robust, and maintainable.
Continuous Learning
- Stay updated with emerging tools, techniques, and best practices in data science, AI/ML, and big data technologies.
Required Qualifications
- Education: Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
- Technical Skills:
- Proficiency in programming languages such as Python, R, and SQL.
- Experience with machine learning libraries and frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
- Familiarity with data visualization tools/libraries (e.g., Tableau, Power BI, Matplotlib, Seaborn).
Preferred Skills
- Knowledge of big data technologies (Hadoop, Spark) and cloud platforms (AWS, Azure, Google Cloud Platform).
- Experience with MLOps practices and deploying models to production.
- Background in domain-specific analytics (e.g., finance, healthcare, telecom) is a plus.
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