Data Scientist

🔥 1 hour ago

Apply Now

Find Similar Remote Jobs

Set Up Email Alert For New Jobs

📊 Check your resume score for this job

Improve your chances of getting an interview by checking your resume score before you apply.

Upload Resume

Gradera

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2025

🤖 Artificial Intelligence

🏢 Enterprise

💼 Consulting

Artificial Intelligence • Enterprise • Consulting

Gradera is an enterprise-focused AI advisory and platform company that delivers "Software-Orchestrated Services™" through its Neural IQ™ intelligence platform. The company builds and deploys reliable, governed digital workers that collaborate with humans, learn contextually, and adapt to deliver measurable outcomes across enterprise value chains. Gradera combines advisory, expert consulting, orchestration, and AI-driven execution to address enterprise complexity, governance, security, and scalability — positioning itself as a partner for large organizations undergoing digital transformation.

📋 Description

• Collect, clean, and analyze large structured and unstructured datasets from multiple internal and external sources • Conduct thorough exploratory data analysis (EDA) to understand data distributions, relationships, outliers, and missing value patterns • Profile and audit datasets to assess data quality, completeness, consistency, and fitness for modeling • Investigate and document data lineage — understanding where data originates, how it flows, and how it transforms across systems • Identify and resolve data anomalies, inconsistencies, and integrity issues in collaboration with data engineering teams • Develop a deep understanding of the business domain and the underlying data that represents it — including what each field means, how it is captured, and what its limitations are • Translate raw, messy, real-world data into clean, well-understood analytical datasets ready for modeling and reporting • Apply statistical techniques such as correlation analysis, hypothesis testing, variance analysis, and distribution fitting to extract meaningful signals from noise • Build and deploy machine learning models including regression, classification, clustering, NLP, and time-series analysis • Design, evaluate, and analyze A/B experiments and controlled tests using causal inference techniques • Develop data-driven recommendations backed by rigorous statistical reasoning • Write clean, production-ready code in Python or R • Collaborate with data engineers to build reliable data pipelines and feature stores • Deploy and monitor ML models using MLOps best practices on cloud infrastructure • Build dashboards and self-serve analytics tools to support stakeholder decision-making

🎯 Requirements

• Strong ability to interrogate unfamiliar datasets and quickly develop a working understanding of their structure, semantics, and quirks • Experience working with messy, incomplete, or poorly documented real-world data • Skilled in identifying hidden patterns, trends, seasonality, and anomalies through visual and statistical exploration • Ability to ask the right questions about data — challenging assumptions, validating sources, and understanding the context in which data was collected • Proficiency in data profiling, descriptive statistics, and summary reporting to communicate the shape and health of a dataset • Experience creating data dictionaries, documentation, and data quality reports to support team-wide data understanding • Comfort working across structured (relational tables), semi-structured (JSON, XML), and unstructured (text, logs, sensor streams) data formats • Proficiency in Python (pandas, NumPy, scikit-learn, PyTorch or TensorFlow) and/or R • Strong SQL skills with hands-on experience in DB2 and SQL Server • Experience with Databricks for large-scale data processing, feature engineering, and model training • Familiarity with cloud platforms: Azure or AWS • Experience with data warehouses and big data platforms (Databricks, Snowflake, or Redshift) • Knowledge of MLOps tools such as MLflow, Kubeflow, or Airflow • Experience with streaming data technologies such as Kafka or Spark • Solid foundation in probability, statistics, linear algebra, and experimental design • Experience with deep learning, NLP, computer vision, or Bayesian methods (Nice to Have)

🏖️ Benefits

• Offers Equity