Digital Mold Data Scientist and Manufacturing Analytics Specialist
Data Science
Data AnalyticsDigital Mold Data Scientist and Manufacturing Analytics Specialist
Job Summary
COAST Systems is seeking a Data Scientist and Manufacturing Analytics Specialist in India to support a global client’s Digital Mold program.
This position will work with large and complex manufacturing, engineering, tooling, maintenance, and quality datasets. The successful candidate will transform fragmented operational data into reliable datasets, dashboards, actionable insights, and continuous-improvement opportunities.
This is a hands-on analytics role requiring close collaboration with engineering, manufacturing, operations, and business stakeholders. The position is particularly suited to someone who can understand a technical manufacturing problem, determine what the data is showing, and communicate practical recommendations that improve performance.
Key Responsibilities
- Collect, prepare, cleanse, normalize, and validate manufacturing and engineering data from multiple sources.
- Establish reliable and repeatable datasets for analytics, reporting, and decision-making.
- Analyze data to identify trends, risks, performance gaps, improvement opportunities, and potential cost savings.
- Develop and maintain dashboards, visualizations, KPI reporting, and business intelligence solutions.
- Analyze maintenance and operational performance using measures such as:
- Mean Time to Repair or MTTR
- Mean Time Between Failures or MTBF
- Overall Equipment Effectiveness or OEE
- Preventive and corrective maintenance performance
- Tool reliability, condition, quality, utilization, and lifecycle indicators
- Work closely with engineering and operations teams to convert technical and operational problems into data-driven solutions.
- Support continuous improvement, operational excellence, and process optimization initiatives.
- Align analyses and recommendations with client goals, priorities, and expected business outcomes.
- Identify relationships among tooling, maintenance, production, quality, sensor, and lifecycle data.
- Present findings clearly to technical and nontechnical stakeholders.
- Help establish consistent data definitions, analytical methods, and reporting standards.
- Support the development of predictive analytics, machine learning, and AI-enabled capabilities where appropriate.
- Learn the COAST software environment and relevant client or third-party systems.
- Help map and connect tool-specific data across systems so that information can be aligned and used consistently.
Required Qualifications
- Bachelor’s or master’s degree in data science, statistics, mathematics, computer science, engineering, operations research, or a related quantitative discipline.
- Strong data science and analytics experience involving large, complex, or multi-source datasets.
- Demonstrated experience with data preparation, cleansing, transformation, normalization, validation, and data-quality management.
- Strong statistical and analytical problem-solving skills.
- Proven experience developing dashboards, data visualizations, KPI reporting, and business intelligence solutions.
- Ability to analyze data and translate findings into clear, practical business or operational recommendations.
- Experience working with technical, engineering, operational, or business stakeholders.
- Strong continuous-improvement and process-optimization mindset.
- Ability to communicate clearly in English with global teams and client stakeholders.
- Ability to work independently, manage priorities, and investigate unclear or incomplete data.
- Strong attention to detail and commitment to data accuracy.
Preferred Qualifications
- Experience analyzing data in a manufacturing, engineering, maintenance, operations, or asset-management environment.
- Understanding of manufacturing equipment, tooling, maintenance, quality, and asset lifecycle concepts.
- Familiarity with MTTR, MTBF, OEE, preventive maintenance, reliability, and related manufacturing KPIs.
- Experience in plastics manufacturing or packaging, including any of the following:
- Injection molding
- Blow molding
- Extrusion blow molding
- Injection stretch blow molding
- Compression molding
- Other polymer-processing operations
- Experience working with cloud-based data platforms or data lake environments.
- Experience combining data from multiple software systems, databases, APIs, files, or vendor platforms.
- Experience with sensor, machine, equipment, IoT, or time-series data.
- Exposure to predictive analytics, machine learning, anomaly detection, forecasting, or AI applications.
- Experience developing analytics that lead to actionable workflows, reduced costs, improved reliability, or reduced manual effort.
- Experience supporting global organizations or working in a client-facing environment.
Technical Skills
Candidates should demonstrate proficiency in several of the following areas:
- SQL
- Python or R
- Statistical analysis
- Data preparation and transformation
- Data validation and data-quality analysis
- Dashboard and visualization development
- Power BI, Tableau, QuickSight, or a comparable BI platform
- Cloud data lakes or cloud analytics environments
- Relational and non-relational data sources
- Advanced Microsoft Excel
- Predictive modeling or machine learning
- API or multi-system data integration
Specific experience with every listed technology is not required. The candidate must, however, have strong foundational analytics skills and the ability to learn unfamiliar platforms and data environments.
Critical Competencies
- Analytical curiosity
- Structured problem-solving
- Systems thinking
- Data accuracy and attention to detail
- Continuous-improvement mindset
- Business and operational awareness
- Clear written and verbal communication
- Cross-functional collaboration
- Client responsiveness
- Adaptability and willingness to learn
- Ability to convert analysis into action
Experience
Approximately 4 to 8 years of relevant professional experience is preferred. Candidates with fewer years may be considered if they demonstrate strong hands-on analytics experience, manufacturing exposure, and the ability to work directly with engineering and operational stakeholders.
What Success Looks Like
The successful candidate will:
- Create trusted and repeatable manufacturing datasets.
- Deliver dashboards and reports that stakeholders actively use.
- Identify meaningful risks, trends, and improvement opportunities.
- Help engineering and operations teams make better decisions from their data.
- Improve the consistency of tool-specific information across systems.
- Progressively develop more advanced predictive and AI-enabled Digital Mold capabilities.
- Produce measurable improvements in reliability, operational performance, cost, and efficiency.
Suggested Key Skills
Data Science, Manufacturing Analytics, Data Analytics, Business Intelligence, Dashboard Development, Data Visualization, Power BI, Tableau, Amazon QuickSight, SQL, Python, R, Statistical Analysis, Data Cleansing, Data Normalization, Data Validation, Data Quality, Manufacturing KPI, OEE, MTTR, MTBF, Predictive Analytics, Machine Learning, Continuous Improvement, Process Optimization, Maintenance Analytics, Reliability Analytics, Cloud Data Lake, Sensor Data, IoT Analytics, Injection Molding, Plastics Manufacturing
Shubham Vishwakarma
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