Entry-Level Data Scientist

The primary focus of this role is to design and develop data pipelines, automate data acquisition, and ingest raw data from diverse sources and formats. This involves transforming, cleansing, and storing data for downstream consumption. The role also encompasses crafting detailed problem statements, formulating hypotheses, and understanding their impact on clients or customers. You will analyze and investigate complex data sets, summarize key features, and transform data into features suitable for machine learning models. Additionally, responsibilities include training, deploying, and maintaining reliable and efficient models in production, verifying model performance, and collaborating with both internal and external stakeholders to deliver data-driven insights.

Key Responsibilities:

• Understand and work with data platforms and applications.
• Strong foundation in machine learning, applied statistics, and experimentation.
• Proficient in statistical and data programming languages, such as PySpark, Spark, and Python.
• Experience handling medium-to-large datasets (over 1 million rows).
• Practical experience with machine learning frameworks like PyTorch, Keras, TensorFlow.
• Competence in SDLC environments and deploying production-grade engineering models.

Required Skills and Experience:

• Independently completes tasks and supports business-specific projects by applying expertise and business knowledge to generate creative solutions.
• Collaborates effectively across teams and external partners to drive informed business decisions, solve complex problems, and escalate critical issues when necessary.
• Designs and develops data pipelines and automation processes for acquiring and ingesting raw data from various sources and formats, while transforming, cleansing, and storing data for further use.
• Analyzes complex data sets, using data visualization to summarize key characteristics and uncover patterns, anomalies, or test hypotheses.
• Converts data into features for machine learning algorithms by utilizing techniques for dimensionality reduction, feature selection, and feature importance.
• Trains statistical models using various algorithms, tests models with different datasets, and applies cross-validation techniques to avoid overfitting.
• Deploys, maintains, and ensures the reliability of models in production environments.
• Verifies model performance by applying validation techniques and continuously improving model accuracy based on feedback.
• Collaborates with stakeholders to derive and present insights from data, aiding in the resolution of complex problems across multiple use cases.

Additional Qualifications:

• Experience with data visualization techniques.
• Strong background in machine learning and algorithm development.
• Proficient in statistical analysis and modeling.
• Minimum of one year of experience with Python coding.
• Bachelor’s degree in Mathematics, Statistics, Computer Science, Engineering, Economics, Public Health, or a related field.