Data Scientist

About Gradera

Gradera is a pioneering AI-native services company specializing in Software-Orchestrated Services™, a novel approach that integrates software, human expertise, digital workers, and enterprise systems to deliver reliable and scalable business outcomes. The firm supports enterprises in moving beyond fragmented AI efforts and disconnected automation to redesign workflows across operations, product development, engineering, customer experience, and data management.

Role Overview

We are looking for a highly analytical and inquisitive Data Scientist to convert complex real-world data into actionable insights and scalable machine learning solutions. This role spans the entire data lifecycle, collaborating with data engineering and business teams to explore, clean, and understand diverse datasets and subsequently deliver models, experiments, and actionable data-driven guidance.

Key Responsibilities

• Gather and preprocess large datasets from various internal and external sources, both structured and unstructured.
• Perform thorough exploratory data analysis to understand distributions, relationships, outliers, and missing value patterns.
• Assess and document data quality, completeness, consistency, and suitability for modeling purposes.
• Trace and document data lineage to comprehend data origins, flow, and transformations across systems.
• Collaborate with data engineering teams to address and resolve data anomalies, inconsistencies, and integrity challenges.
• Develop in-depth knowledge of the business domain and the data representations, including the meanings and capture methods of each data field as well as inherent constraints.
• Transform raw and messy data into clean, well-defined analytical datasets suitable for modeling and reporting.
• Utilize statistical methods such as correlation analysis, hypothesis testing, variance analysis, and distribution fitting to extract relevant signals.
• Create and deploy machine learning models encompassing regression, classification, clustering, NLP, and time-series analysis.
• Design, conduct, and analyze controlled experiments like A/B tests using causal inference methodologies.
• Provide data-informed recommendations grounded in rigorous statistical analysis.
• Write maintainable, production-quality code primarily in Python or R.
• Work in conjunction with data engineers to develop reliable data pipelines and feature stores.
• Implement and oversee ML models with MLOps best practices using cloud infrastructure.
• Design and maintain dashboards and self-service analytics tools to aid decision-makers.

Data Understanding & Analytical Competencies

• Expertise in rapidly assimilating unfamiliar datasets, understanding their structure, semantics, and peculiarities.
• Experience handling real-world datasets that are incomplete, messy, or poorly documented.
• Competence in detecting hidden patterns, seasonal trends, and anomalies through statistical and visual data exploration.
• Adept in posing insightful questions regarding data origin, collection context, and validating assumptions.
• Skilled in data profiling, descriptive statistics, and summarizing datasets to convey their status and quality.
• Experience preparing comprehensive data dictionaries, documentation, and quality reports to promote collective understanding.
• Comfortable working across various formats including structured relational tables, semi-structured JSON/XML, and unstructured text or sensor data.

Technical Skills Required

• Proficiency in Python (including pandas, NumPy, scikit-learn, PyTorch, TensorFlow) and/or R programming.
• Strong SQL capabilities with practical experience on DB2 and SQL Server platforms.
• Hands-on experience using Databricks for enterprise-scale data processing, feature engineering, and model training.
• Familiarity with cloud services such as Azure or AWS.
• Experience with data warehouses and big data solutions like Databricks, Snowflake, or Redshift.
• Knowledge of MLOps frameworks such as MLflow, Kubeflow, or Airflow.
• Experience working with streaming data tools including Kafka or Spark.
• Solid foundation in core math and statistics disciplines including probability, linear algebra, and experimental design.

Desirable Qualifications

• Experience with advanced topics including deep learning, natural language processing, computer vision, or Bayesian methods.
• Familiarity with real-time and streaming data pipelines.
• Participation in open-source contributions or academic research publications.

Compensation

The salary range for this position is $100,000 to $145,000 annually.