Job Description:
Basic Qualifications:
Proficiency in programming languages such as Python, SQL, R etc.
Bachelor or Master's degree in highly quantitative field (CS, machine learning, mathematics, statistics) or equivalent experience.
4-6 years of academic and relevant industry experience.
Proven experience as a Data Scientist with a strong portfolio of data science projects.
Experience in a specific industry (e.g., Edtech, finance,retail) is advantageous.
Experience with data manipulation and analysis libraries (e.g., Pandas, NumPy, Scikit-learn).
Familiarity with big data technologies (e.g., Hadoop, Spark) is a plus.
Knowledge of data visualization tools (e.g., Matplotlib, Seaborn, D3.js).
Hands-on experience with Databricks for big data processing and analytics.
Proficiency with the Elastic Stack (Elasticsearch & Kibana) for search and data analysis is a plus.
Analytical Skills: Strong ability to analyze complex data sets, identify trends, and draw actionable conclusions.
Communication Skills: Excellent verbal and written communication skills, with the ability to convey technical information to non-technical stakeholders.
Problem-Solving: Demonstrated ability to tackle complex problems and develop innovative solutions.
Preferred Qualifications:
Bachelor's, Master's or PhD degree in CS, Statistics, Applied Math, Operations Research, Economics, or a related quantitative field with at least 3 years of working experience as a Data Scientist.
Experience with cloud platforms (e.g., AWS, Azure, Google Cloud).
Knowledge of advanced machine learning techniques (e.g., deep learning, natural language processing).
Familiarity with business intelligence tools and methodologies.
Position Overview:
We are seeking a highly skilled Data Scientist to join our team. The ideal candidate will have a strong background in data analysis, statistical modeling, and machine learning, coupled with excellent problem-solving abilities and a keen business sense. You will work closely with cross-functional teams to extract insights from complex datasets and drive data-driven decision-making.
Key Responsibilities:
Data Analysis: Collect, clean, and preprocess large datasets from various sources. Perform exploratory data analysis to uncover patterns and insights.
Statistical Modeling: Develop and implement statistical models and algorithms to address business problems. Validate models and perform performance evaluations.
Machine Learning: Design, train, and deploy machine learning models to predict outcomes and automate processes. Continuously improve model accuracy and efficiency.
Collaboration: Work with data engineers, analysts, and other team members to understand business requirements and provide data-driven solutions.
Continuous Learning: Stay updated with the latest industry trends, technologies, and best practices in data science and machine learning.
Skill:
AWS, Azure, Google Cloud,
