This is a remote position.
Job Summary
We are seeking a skilled Data Scientist to analyze complex datasets, develop predictive models, and generate actionable insights that support business decisions. The successful candidate will work with cross-functional teams to identify trends, solve business problems, and improve products, processes, and overall performance through data-driven solutions.
Key Responsibilities
- Collect, clean, transform, and analyze structured and unstructured data from multiple sources.
- Develop and implement statistical models, machine learning algorithms, and predictive analytics solutions.
- Identify patterns, trends, and opportunities within large datasets and translate findings into actionable insights.
- Build and evaluate data models to support forecasting, classification, segmentation, and business decision-making.
- Create dashboards, reports, and visualizations to communicate complex findings clearly to technical and non-technical stakeholders.
- Collaborate with engineering, product, business, and analytics teams to define data requirements and solve business problems.
- Monitor model performance and improve analytical solutions as business needs and datasets evolve.
- Document methodologies, assumptions, analyses, and results to support reproducibility and informed decision-making.
Qualifications
- Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field.
- 2+ years of experience in data science, data analytics, machine learning, or a related field.
- Strong proficiency in Python or R for data analysis and machine learning.
- Experience with SQL and relational databases.
- Solid understanding of statistics, probability, machine learning, and data modeling concepts.
- Experience working with data visualization and reporting tools.
- Strong analytical, problem-solving, and communication skills.
- Ability to work independently while collaborating effectively with cross-functional teams.
Preferred Skills
- Experience with machine learning frameworks such as Scikit-learn, TensorFlow, or PyTorch.
- Knowledge of cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
- Experience with big data technologies such as Spark or Hadoop.
- Familiarity with Tableau, Power BI, or similar business intelligence tools.
- Experience with A/B testing, experimentation, and advanced statistical analysis.
- Knowledge of data engineering concepts, ETL pipelines, and data warehousing.
- Understanding of MLOps, model deployment, monitoring, and lifecycle management.
- Ability to communicate technical findings effectively to senior leadership and business stakeholders.
