SENIOR DATA ANALYST
About this role
Position: Senior Data Analyst
Job Type: (Remote)
Job Summary
The Senior Data Analyst is responsible for applying advanced analytical techniques, statistical methods, and data-driven approaches to solve complex business problems. This position analyzes large and diverse datasets, develops predictive and prescriptive insights, builds analytical models, and partners with business and technical teams to support strategic decision-making.
The Senior Data Analyst will work closely with data scientists, data engineers, business leaders, and other stakeholders to identify opportunities, improve processes, develop predictive insights, and translate complex analytical findings into clear business recommendations.
Key Responsibilities
Analyze large and complex datasets to identify trends, patterns, relationships, and business opportunities.
Apply statistical and advanced analytical techniques to support business decision-making.
Develop predictive models, forecasts, segmentation analyses, and other advanced analytics solutions.
Use SQL, Python, R, or similar analytical tools to collect, transform, analyze, and validate data.
Develop analytical models and methodologies to address business and operational challenges.
Perform exploratory data analysis and identify meaningful relationships and patterns within datasets.
Design and evaluate experiments, A/B tests, and statistical analyses when appropriate.
Develop and monitor analytical models and performance metrics.
Partner with data scientists and data engineers to prepare high-quality datasets for advanced analytics and machine learning initiatives.
Translate business questions and requirements into analytical approaches and measurable outcomes.
Communicate complex analytical findings to business leaders and non-technical stakeholders in a clear and understandable manner.
Create reports, visualizations, presentations, and analytical dashboards to communicate insights.
Conduct root-cause analysis and provide data-driven recommendations for business improvement.
Identify opportunities to automate analytical processes and improve data workflows.
Validate data quality, model outputs, and analytical results to ensure accuracy and reliability.
Document analytical methodologies, assumptions, data sources, and results.
Support machine learning and artificial intelligence projects through data preparation, feature analysis, model evaluation, and performance monitoring.
Stay current with analytical techniques, statistical methodologies, and emerging data science technologies.
Mentor junior analysts and contribute to analytical best practices across the organization.
Qualifications
Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Business Analytics, or a related field.
Master's degree in Data Science, Statistics, Analytics, or a related discipline is preferred.
4–7 years of experience in data analytics, advanced analytics, data science, or a related field.
Strong SQL skills and experience working with relational databases and large datasets.
Proficiency in Python, R, or another programming language used for data analysis.
Strong knowledge of statistical analysis, predictive modeling, and data visualization.
Experience with data manipulation, exploratory data analysis, and statistical modeling.
Experience working with BI and visualization tools such as Power BI, Tableau, or similar platforms.
Strong understanding of data quality, data preparation, and analytical methodology.
Excellent analytical, problem-solving, and critical-thinking skills.
Ability to communicate technical findings effectively to both technical and non-technical audiences.
Preferred Qualifications
Experience with machine learning algorithms and model evaluation.
Knowledge of regression, classification, clustering, time-series analysis, and forecasting.
Experience with cloud data platforms such as AWS, Azure, or Google Cloud.
Experience with data warehouses such as Snowflake, Databricks, BigQuery, or similar platforms.
Familiarity with machine learning frameworks such as scikit-learn, TensorFlow, or PyTorch.
Experience with A/B testing, experimental design, and causal analysis.
Knowledge of data engineering, ETL/ELT processes, and data pipelines.
Experience working with large-scale or real-time datasets.
Familiarity with AI and machine learning applications in business environments.
Key Skills
Advanced Data Analytics
Statistical Analysis
Predictive Modeling
Machine Learning
SQL
Python / R
Exploratory Data Analysis
Forecasting
Data Visualization
Experimental Design and A/B Testing
Feature Engineering
Data Modeling
Data Quality and Validation
Business Problem Solving
Artificial Intelligence
Advanced Excel
Analytical Storytelling
Cross-Functional Collaboration
Core Competencies
Analytical Thinking: Ability to investigate complex problems and develop data-driven solutions.
Business Acumen: Ability to understand business objectives and translate them into measurable analytical questions.
Technical Expertise: Ability to work with advanced analytical tools, programming languages, databases, and statistical methods.
Communication: Ability to explain complex analytical results clearly to executives, business users, and technical teams.
Problem Solving: Ability to identify root causes, evaluate alternatives, and recommend evidence-based solutions.
Leadership: Ability to lead analytical projects and mentor junior team members.
Originally posted on Himalayas
