Data Analyst

At Nineleaps, we work on bleeding-edge technology with class-leading engineering practices on products that touch the lives of millions of users. We endeavor on doing things the right way, while also promoting a culture of excellence.

About the Role:

We are looking for a Data Analyst with strong analytical and problem-solving skills to transform complex data into meaningful, actionable business insights. The role involves working with large datasets, conducting deep-dive analysis, driving automation, and supporting data-driven product and business decisions.

Key Responsibilities:

• Analyse historical and large datasets to understand data sources, identify trends and patterns, and uncover meaningful insights.
• Write complex SQL queries and leverage Python to perform data analysis, ad hoc investigations, and solve business problems.
• Create reports and translate analytical findings into clear, concise, and actionable recommendations for stakeholders.
• Identify opportunities to drive automation and process improvements, improving efficiency and reducing manual effort.
• Communicate data-driven insights effectively to both technical and non-technical stakeholders in a clear and impactful manner.
• Maintain accurate documentation, ensure high-quality deliverables, and consistently meet defined timelines.

Requirements:

• 3–6 years of experience in Data Analytics, Business Intelligence, Data Engineering, or a similar analytical role.
• Strong hands-on expertise in Python and advanced SQL, with the ability to work with and analyse large datasets.
• Experience working with Google Sheets, and implementing automation through data pipelines or workflows.
• Strong analytical and problem-solving skills, with the ability to interpret complex data and derive actionable insights.
• Excellent communication skills with the ability to effectively present methods, results, and recommendations to stakeholders.
• Ability to collaborate effectively with remote and geographically distributed teams across different time zones.

Company Link: https://www.nineleaps.com/

Company LinkedIn: https://www.linkedin.com/company/nineleaps/

We’re looking for a dynamic and driven Data Analyst to join our team of technology enthusiasts. This role is crucial in transforming data into insights that support strategic decision-making and innovation within the insurance technology (InsurTech) space. If you’re passionate about working with data, understanding systems, and delivering value through analytics, we’d love to hear from you.

What We’re Looking For

• Proven experience working as a Data Analyst or in a similar analytical role
• 3+ Years of experience in the field
• Strong command of SQL for querying and manipulating relational databases
• Experience with Power BI for building impactful dashboards and reports
• Familiarity with QlikView and Qlik Sense is a plus
• Ability to communicate findings clearly to technical and non-technical stakeholders
• Knowledge of Python or R for data manipulation is nice to have
• Bachelor’s degree in Computer Science, Statistics, Mathematics, Economics, or a related field
• Understanding of the insurance industry or InsurTech is a strong advantage

What You’ll Be Doing:

• Delivering timely and insightful reports to support strategic decision-making
• Working extensively with Policy Administration System (PAS) data to uncover patterns and trends
• Ensuring data accuracy and consistency across reports and systems
• Collaborating with clients, underwriters, and brokers to translate business needs into data solutions
• Organizing and structuring datasets, contributing to data engineering workflows and pipelines
• Producing analytics to support business development and market strategy

Strong Data Analyst Profile with advanced Excel and SQL expertise

2

Mandatory (Experience 1): Must have 4+ years of overall experience as a hands-on Data Analyst

3

Mandatory (Tech skill 1): Must be highly proficient in advanced Excel — complex functions, macros, calculations, and pivots

4

Mandatory (Tech skill 2): Must have strong hands-on SQL and a good understanding of relational database concepts

5

Mandatory (Tech skill 3): Must be able to automate routine tasks using Python (for automation purposes)

6

Mandatory (Skill 1): Must have exceptional analytical, problem-solving, and logical skills, with strong attention to detail and accuracy

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Mandatory (Skill 2): Must be able to understand complex data and business logic and convert it into a model (the role models complex utility tariffs, rates, and programs)

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Mandatory (Communication): Must have strong verbal and written communication, able to work independently with India- and US-based team members and articulate problems and solutions over calls and email.

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Mandatory (Location): Must be based locally in Pune (or the nearby Maharashtra belt — Mumbai, Nagpur), as the final round is in person

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Preferred (Domain): Experience in the Energy/Utility industry and familiarity with basic utility (electrical/gas) tariff concepts

Hiring for Data Scientist / Senior Data Scientist

Exp : 4 - 12 yrs

Edu : BE/B.tech/MCA

Work Location : Pune

Notice Period : Immediate - 15 days

Skills :

4+ years of experience in data engineering, data science, or related domains.

Hands-on experience with SQL, Python, and distributed data systems.

Knowledge of machine learning techniques and statistical analysis.

Experience with cloud data platforms (Azure Data Factory, AWS Glue, GCP BigQuery).

Familiarity with DevOps practices and CI/CD for data pipelines.

Platforms & Operations Experience (Preferred)

- Experience working with Azure, AWS, or Google Cloud data tools.

Operational experience with data orchestration tools (Airflow, ADF, Glue).

Understanding of Kubernetes, Docker, or containerized environments.

Hands-on experience with data warehousing platforms (Snowflake, Redshift, BigQuery).

Experience in monitoring, logging, and alerting operations for data workflows.

Sr.Data Scientist,Python, AI ML

We are looking for a skilled Data Scientist to analyze complex datasets, develop predictive models, and generate actionable insights that support business decisions. The ideal candidate should have strong statistical, analytical, and programming skills, along with hands-on experience in machine learning.

Role Overview 

As a Data Scientist, you will work with business stakeholders, AI engineers, and domain experts to transform data into actionable insights and intelligent solutions. You will develop machine learning models, perform statistical analysis, and contribute to AI-driven products that create measurable business impact. 

Key Responsibilities 

Data Science & Machine Learning 

• Analyze structured and unstructured data to identify patterns, trends, and business opportunities.  
• Perform exploratory data analysis (EDA), feature engineering, and data preparation.  
• Develop, evaluate, and optimize machine learning models for prediction, classification, clustering, and forecasting.  
• Apply statistical techniques to solve business problems and validate model performance.  
• Design and execute experiments to improve model accuracy and business outcomes.  

 AI Solution Development 

• Collaborate with AI Engineers, Data Engineers, and domain experts to build AI-powered solutions.  
• Translate business requirements into scalable data science approaches.  
• Contribute to Generative AI and advanced analytics initiatives where applicable.  
• Document methodologies, model performance, and key findings.  

 Required Technical Skills 

• Strong programming skills in Python and SQL for data analysis, feature engineering, and machine learning.  
• Strong understanding of Statistics, Probability, Linear Algebra, and Calculus as applied to machine learning and data science.  
• Experience with Exploratory Data Analysis (EDA), data preprocessing, feature engineering, feature selection, and handling missing or imbalanced data.  
• Good understanding of Supervised, Unsupervised, and Ensemble Machine Learning algorithms, including their assumptions, strengths, limitations, and appropriate use cases.  
• Strong knowledge of Regression, Classification, Clustering, Time Series Forecasting, Dimensionality Reduction, Recommendation Systems, and Anomaly Detection techniques.  
• Experience with Model Evaluation, Cross-Validation, Hyperparameter Optimization, Bias-Variance Trade-off, Feature Importance, Explainable AI (XAI), and Performance Metrics.  
• Understanding of Statistical Inference, Hypothesis Testing, Probability Distributions, Sampling Techniques, Confidence Intervals, and A/B Testing.  
• Experience translating business problems into analytical approaches and developing scalable, data-driven solutions.  
• Working knowledge of Generative AI, Large Language Models (LLMs), Prompt Engineering, and Retrieval-Augmented Generation (RAG) is preferred. 

Preferred Qualifications 

• Bachelor's or master's degree in computer science, Artificial Intelligence, Data Science, Statistics, Mathematics, Engineering, or a related field.  
• 2–4 years of experience developing machine learning or data science solutions.  
• Experience working on end-to-end data science projects in a business environment.  

 Nice to Have 

• Exposure to Generative AI, LLMs, RAG, or Agentic AI.  
• Experience with Computer Vision or Natural Language Processing (NLP).  
• Familiarity with cloud-based AI platforms.  
• Knowledge of construction, engineering, manufacturing, or industrial domains.  
• Participation in hackathons, research, Kaggle competitions, or open-source projects.  

 Soft Skills 

Strong analytical and problem-solving skills, effective communication and collaboration, ownership mindset, adaptability, continuous learning, and a passion for innovation.

We're hiring a Data Analyst to turn raw data into clear, actionable insight. You'll partner with product, sales, marketing, and operations teams to answer their most important questions with data — building dashboards, running deep-dive analyses, and defining the metrics the business runs on. The ideal candidate is fluent in SQL, comfortable wrangling messy datasets, and just as strong at telling the story behind the numbers as they are at producing them. You'll own the accuracy and trustworthiness of the reporting stakeholders rely on to make decisions.

Key Responsibilities

• Design, build, and maintain dashboards and recurring reports across business functions
• Write and optimize SQL queries to extract, join, and transform data from multiple sources
• Run ad-hoc and deep-dive analyses to answer specific business questions
• Define, document, and standardize metrics and KPIs alongside stakeholders
• Investigate data-quality issues and ensure the numbers people see are accurate
• Translate analysis into clear recommendations and present them to non-technical audiences
• Support experimentation and A/B test analysis where relevant
• Automate repetitive reporting to free up time for higher-value analysis

Requirements

• 3+ years in a data analyst or business-intelligence role
• Strong SQL and hands-on experience with Power BI and/or Tableau
• Working knowledge of Python (pandas/numpy) for analysis
• Solid grounding in statistics and analytical methods
• Advanced Excel and strong data-visualization / storytelling skills
• Ability to work independently with stakeholders across functions

Nice to have

• Experience with cloud data warehouses (BigQuery, Snowflake, Redshift)
• Exposure to dbt or other ETL/transformation tooling

Key Responsibilities

• Collect, consolidate and manage data from various internal systems, SaaS platforms, CRM/ERP modules, logs, and databases.
• Generate daily, weekly, monthly, and ad-hoc MIS reports and dashboards for operations, sales, finance, customer-support or other relevant teams.
• Use advanced Excel to build, maintain, and manage complex spreadsheets — including pivot tables, VLOOKUP / HLOOKUP / INDEX-MATCH, SUMIF/COUNTIF, conditional formatting, charts/graphs, macros/VBA (if needed) to automate routine reporting tasks.
• Validate and clean data — ensure data integrity, consistency; identify and rectify discrepancies or anomalies.
• Collaborate with cross-functional teams (product, operations, support, finance, sales) to understand their data/reporting needs and deliver appropriate reports/insights.
• Provide ad-hoc data analysis or custom reporting as required by management or US stakeholders.
• Maintain documentation of reporting processes, data definitions/SOPs, report templates, and standard workflows.
• Ensure timely delivery of reports — especially considering night-shift schedule — so that US-based stakeholders receive data at start of their business day.
• Identify opportunities for process improvement and automation to make MIS reporting more efficient and reliable.

Required Skills & Qualifications

• Bachelor’s degree (in IT, Computer Science, Business, Statistics, or related field) or equivalent.
• Proven experience (1–3 years or as per company requirement) in MIS, data reporting/analysis, or similar role.
• Strong proficiency in Microsoft Excel — including advanced formulas/functions (VLOOKUP/HLOOKUP/INDEX-MATCH), pivot tables, charts/graphs, conditional formatting, data cleaning, data validation.
• Ability to build and maintain dashboards/reports; comfortable using Excel for recurring and ad-hoc reports.
• Strong analytical and problem-solving skills; ability to work with large datasets, identify trends, anomalies, and draw insights.
• Good communication skills — ability to coordinate with different teams/stakeholders, understand requirements, and present data/insights clearly.
• Readiness for night-shift work from office; ability to work independently, meet deadlines and manage time effectively.

Digital 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

Your Experience at a Glance

We’re hiring a Data Scientist for our client delivers advanced data, analytics, and digital transformation solutions to help organizations modernize and drive business insights.

As a Data Scientist, you will play a key role in developing and deploying demand forecasting and pricing models, leveraging advanced statistical and machine learning techniques. You will collaborate closely with data engineers and business stakeholders to extract, transform, and analyze large datasets, ensuring robust and scalable solutions. This position requires strong ownership of model development, from data pipeline integration to model evaluation and reporting. Your work will directly impact business decision-making and operational efficiency, contributing to KPIP’s mission of enabling data-driven transformation.

KPIP is a global consulting and technology services firm specialising in data, analytics, and digital transformation. Serving a diverse range of industries, KPIP empowers organisations to modernize their data ecosystems and unlock actionable business insights. The company is recognized for its expertise in delivering scalable solutions, fostering a culture of innovation, and driving measurable impact for clients worldwide.

Key Responsibilities

● Develop and implement demand forecasting and pricing models using advanced statistical and machine learning techniques.

● Extract, transform, and analyze large datasets using Python and SQL to support model development and business insights.

● Collaborate with data engineers to build and maintain robust, scalable data pipelines for model training and inference.

● Apply regression, classification, time-series forecasting, ensemble methods, and feature engineering to solve business problems.

● Work with business stakeholders to understand requirements and translate them into actionable data science solutions.

● Create automated reports and dashboards to present and track model outputs and performance.

● Continuously evaluate and improve model accuracy and effectiveness based on business feedback and new data.

● Document methodologies, processes, and results to ensure transparency and reproducibility.

● Stay updated with the latest advancements in data science and machine learning to drive innovation within the team.

Required Skills

● Proven experience in demand forecasting and predictive modeling.

● Strong proficiency in Python, including pandas, NumPy, scikit-learn, and TensorFlow or PyTorch.

● Expertise in SQL for data extraction and transformation.

● Solid understanding of statistical and machine learning techniques such as regression, classification, time-series forecasting, ensemble methods, and feature engineering.

● Ability to analyze and interpret large, complex datasets to generate actionable insights.

● Experience collaborating with data engineers to develop scalable data pipelines.

● Strong problem-solving skills and attention to detail.

● Excellent communication skills for presenting technical concepts to non-technical stakeholders.

Nice to Have

● Experience with customer segmentation, recommendation systems, and sentiment analysis.

● Knowledge of inventory optimization, promotion uplift modeling, and campaign analysis.

● Familiarity with churn prediction models.

● Proficiency in Power BI for creating automated reports and dashboards.

● Experience in developing and maintaining data pipelines for model training and inference.

Why Join?

Join to work on impactful data science projects that drive real business outcomes and innovation. You’ll tackle complex technical challenges, collaborate with talented professionals, and have opportunities for continuous learning and growth, fosters a culture of collaboration, excellence, and data-driven decision-making, empowering you to make a meaningful difference in a dynamic environment.

About the Employment Model

Direct Hire (Client Payroll) : For this role, you’ll be hired directly by the client and be part of their internal team. Straatix supports the hiring process, but your employment, payroll, and benefits are all managed by the client.