Sr. Data Scientist

Role :: Data Scientist

Location :: Tampa, FL/Dallas ,TX - Onsite

Type :: Fulltime

Job Description

Must Have Technical/Functional Skills

• Programming & Libraries: Expert-level proficiency in Python and its core data science libraries (Pandas, NumPy, Scikit-learn). Strong proficiency in SQL for complex data extraction and manipulation.
• Machine Learning Frameworks: Hands-on experience with modern deep learning frameworks such as TensorFlow or PyTorch.
• Statistical Modeling: Deep understanding of statistical concepts and a wide range of machine learning algorithms, with proven experience in time-series forecasting and anomaly detection.
• Big Data Technologies: Demonstrable experience working with large datasets using distributed computing frameworks, specifically Apache Spark.
• Database Systems: Experience querying and working with data from multiple relational database systems (e.g., PostgreSQL, Oracle, MS SQL Server).
• Cloud Platforms: Experience building and deploying data science solutions on a major cloud platform (AWS, Google Cloud Platform, or Azure). Familiarity with their native ML services (e.g., AWS SageMaker, Google Vertex AI) is a strong plus.
• MLOps Tooling: Practical experience with MLOps principles and tools for model versioning, tracking, and deployment (e.g., MLflow, Docker).
• Communication and Storytelling: Excellent verbal and written communication skills, with a proven ability to explain complex technical concepts to a non-technical audience through visual storytelling.

Roles & Responsibilities

Druid Data Modeling & Schema Design:

o Design and implement efficient data schemas, dimensions, and metrics within Apache Druid for various analytical use cases (e.g., clickstream, IoT, application monitoring).

o Determine optimal partitioning, indexing (bitmap indexes), and rollup strategies to ensure sub-second query performance and efficient storage.

Data Ingestion Pipeline Development:

o Develop and manage real-time data ingestion pipelines into Druid from streaming sources like Apache Kafka, Amazon Kinesis, or other message queues.

o Implement batch data ingestion processes from data lakes (e.g., HDFS, Amazon S3, Azure Blob, Google Cloud Storage) or other databases.

o Ensure data quality, consistency, and exactly-once processing during ingestion.

Query Optimization & Performance Tuning:

o Write and optimize complex SQL queries (Druid SQL) for high-performance analytical workloads, including aggregations, filters, and time-series analysis.

o Analyze query plans and identify performance bottlenecks, implementing solutions such as segment optimization, query rewriting, or cluster configuration adjustments.

Programming & Libraries: Expert-level proficiency in Python and its core data science libraries (Pandas, NumPy, Scikit-learn). Strong proficiency in SQL for complex data extraction and manipulation.

Machine Learning Frameworks: Hands-on experience with modern deep learning frameworks such as TensorFlow or PyTorch.

Statistical Modeling: Deep understanding of statistical concepts and a wide range of machine learning algorithms, with proven experience in time-series forecasting and anomaly detection.

Big Data Technologies: Demonstrable experience working with large datasets using distributed computing frameworks, specifically Apache Spark.

Database Systems: Experience querying and working with data from multiple relational database systems (e.g., PostgreSQL, Oracle , MS SQL Server).

Cloud Platforms: Experience building and deploying data science solutions on a major cloud platform (AWS, Google Cloud Platform, or Azure). Familiarity with their native ML services (e.g., AWS SageMaker, Google Vertex AI) is a strong plus.

MLOps Tooling: Practical experience with MLOps principles and tools for model versioning, tracking, and deployment (e.g., MLflow, Docker).

Communication and Storytelling: Excellent verbal and written communication skills, with a proven ability to explain complex technical concepts to a non-technical audience through visual storytelling