Mining Drill Data Optimization

@Microlent

Hello, I am Dr. Rajesh Rolen, PhD in Computer Science & Engineering, with experience of over 20+ years in SQL, Data Visualization, Python As a preferred freelancer in the top 1%, I have done 400+ projects here on freelancer.com, I have 4.9 ratings out of 5 on average, which showcases my quality of work and timely delivery. Key Highlights: - Free Hosting Support on the Cloud or any desired platform. - Free 3 months of post-delivery support to ensure that our client doesn’t face any challenges after the launch of the project. - Free Dedicated tester on projects to ensure quality delivery, so clients don’t need to act as a tester. - 10+ Years experience UI/UX team to ensure intuitive UI. Portfolio: https://www.freelancer.com/u/Microlent Please open the chat and send me a message, so we can have a more detailed discussion about the project to give you the project timeline and cost. Thank you for considering my services. I look forward to engaging in a productive conversation and understanding how I can be of assistance in bringing your project to life. Regards Rajesh Rolen

@OutsourceMan

Hello Valuable Client, CnEL India can transform your drill and operational data into a repeatable optimization workflow focused on measurable operational improvements. • Begin by profiling and cleaning fleet logs, sensor time-series, and geology data, handling missing values, timestamps, and data quality issues. • Build a documented Python pipeline/notebook that can process fresh datasets consistently. • Analyze cycle times, idle hours, equipment utilization, pressure/torque patterns, and maintenance indicators to identify bottlenecks. • Develop predictive models for downtime/maintenance risks and prescriptive analysis to identify opportunities for throughput and cost improvement. • Create Power BI/Tableau dashboards with refreshable KPIs, bottlenecks, trends, and actionable recommendations. • Validate findings against operational data and quantify potential impact. • Deliver a concise report highlighting at least three data-backed optimization opportunities. • Test the complete workflow on a fresh dataset to ensure reproducibility. We will keep the solution modular and documented so it can evolve as your data archive grows. Best regards, CnEL India

@EightVeer

Hi there, We can help turn the drill-hole, fleet, and sensor tables into a decision-ready analysis focused on cycle time, idle time, and maintenance signals. Our approach would start with data profiling and cleanup, then move into KPI design, bottleneck analysis, and a repeatable Python/SQL pipeline that supports refreshed outputs. We would also structure the dashboard around operational views supervisors can use quickly. One point to confirm is whether the priority is a single site or a multi-site comparison, as that affects the modelling and dashboard structure. The platform bid covers an initial phase focused on A standalone data audit and optimization blueprint covering source profiling, cleaning rules, KPI definition, and a short prioritized analysis of the highest-value drill-cycle and maintenance bottlenecks.; wider implementation would be separately scoped on Freelancer. Best Regards, 8veer

@DeveloperStation

Hi, The pipeline part is where most of this lives. I've built Python data pipelines with Flask backends that ingest raw sources, clean them, and output repeatable analyses, so cleaning your CSV fleet exports plus pressure and torque time series into one structured store is familiar ground. One question before scoping: are the sensor feeds timestamped and aligned across rigs, or do they need resampling to a common interval? That decision drives whether cycle-time and idle-hour metrics are trustworthy, and it shapes any predictive maintenance model on top. I'll be straight that mining telemetry is not in my prior contracts, but the AI-driven analysis and dashboard work is. One example: Descripio: an AI platform turning raw data into actionable optimization insights. Can you share one sample export so I can confirm field structure first? Adil

@TeraByte26

Hi Efraim, I will build a documented Python pipeline that ingests your CSV drill logs, pressure/torque time‑series and geology files, produces repeatable analyses, auto‑refreshing Tableau dashboards, and a concise report with three immediate optimization actions. I can deliver a working prototype within 10 days. Shall I start with a sample dashboard? Thanks, Waiting for your response in chat! Best Regards.

@mayr81

Transform drill-hole and sensor logs into an end-to-end optimization pipeline that field supervisors can use daily. I’ll clean and structure CSV exports, time-series (pressure/torque), and geology logs into a repeatable Python/R/SQL workflow, then produce KPI baselines for cycle time, idle hours, and maintenance lead indicators. Next, I’ll move quickly from descriptive insights to predictive/prescriptive modelling that identifies bottlenecks and quantifies where scheduling and operating parameter changes reduce downtime and cost. Outputs include refreshable dashboards in Tableau/Power BI (KPIs, drift/anomaly views, and actionable “next actions”), plus a concise operational report with at least three data-backed optimization wins. Acceptance is ensured by running the pipeline end-to-end on a fresh dataset, enabling unattended dashboard refresh, and delivering recommendations tied directly to measurable throughput improvements.

@ganeshrasekar

With a broad spectrum of skills that neatly fit into your project requirements, I, Ganesh, am undoubtedly your go-to guy for this task. My experience with backend technologies like Python, SQL and R coupled with my expertise in harnessing crucial KPIs from raw data to drive informed decisions make me an excellent contender for transforming your drill data into actionable insights. Not only will I meticulously clean and structure your datasets but I will also create a well-documented data pipeline/ notebook to ensure all analyses are repeatable. Using my proficiency in Power BI, I will develop visual dashboards that clearly highlight bottlenecks and provide concrete suggestions for process adjustments. But it doesn't end there - I understand the importance of translating complex results into comprehensible language for day-to-day use. Accordingly, I will collate all significant findings in a concise report, tailored specifically for your field supervisors to implement immediately.

@melopgabriel

Hi, your goal is clear: turn drill-hole, sensor, and fleet logs into repeatable operational decisions that reduce idle time and downtime. I’d build a clean pipeline first, then move quickly into KPIs, bottleneck analysis, and predictive signals that show where cycle times and maintenance gaps are hurting throughput. I’ve worked with messy CSV, time-series, and operational datasets in Python, SQL, and R, and I’m comfortable structuring them into something reliable and easy to refresh. For dashboards, I can use Tableau or Power BI to surface the metrics supervisors actually need, not just charts. My approach would be to clean and standardize the data, validate the refresh flow on a fresh dataset, then layer in the models and recommendations that highlight at least three practical wins. If you’d like, I can outline the first version quickly. Best regards, Gabriel

@gotucat

Hi, I reviewed your request to optimize drill-hole and operational logs so you can tighten cycle times, cut idle hours, and schedule maintenance before downtime. I’ll clean and structure the CSV exports, time-series sensor feeds, and geology logs into a repeatable dataset that supports end-to-end analysis. Using Data Mining, Data Visualization, and Data Science, I’ll build an SQL-based pipeline to ingest raw files, generate descriptive summaries, and then move into predictive/prescriptive modeling to pinpoint bottlenecks and throughput opportunities. I’ll deliver well-documented notebooks or pipelines, dashboards that refresh automatically, and a supervisor-ready report with at least three actionable optimization wins backed by data. Let’s discuss here now.

@codeandcommand

The drill-hole and operational logs are the real challenge here since mining data is usually messy across sites and formats. I would set up a Python and SQL pipeline to clean and structure it, then build visualizations that actually surface patterns your team can act on. Can start today, working version in 4 to 5 days. Bid and timeline are starting points based on the post, we will firm them up once I see a sample of the data. Want me to send a quick scope doc?

@Georgeoustar

Hello, As a senior full-stack developer with a flair for data analysis, mining and visualization, I am confident of being your best fit to optimize your mining drill data. Over my 15+ year career, I have held central positions in various development life cycles revolving around robust data-driven systems. It includes powerful back end infrastructure and intuitive front end dashboards similar to what this project demands. My extensive work with cloud infrastructure (AWS) and REST API & GraphQL integrations is especially relevant in creating the seamless pipeline you need. While my core skill lies in code and its implementation, I pride myself on understanding and unlocking actionable insights out of complex datasets in Python and SQL. That's where we can have the most significant strides towards maximizing throughput while minimizing operational costs, a hiring criterion you explicitly outlined. In conclusion, choosing me as your mining drill data optimization specialist ensures not just a streamlined workflow and insightful visualizations but a comprehensible, practical report that can enable prompt action from your field supervisors. We have a shared vision of converting raw tables into clear, actionable insights, I aim to make it happen for you! Thanks!

@dataspro

Nice to meet you , It is a pleasure to communicate with you. My name is Anthony Muñoz, I am the lead engineer for DSPro IT agency and I would like to offer you my professional services. I have more than 10 years of working as a Backend and Software developer, I have successfully completed numerous jobs similar to yours therefore, and after carefully reading the requirements of your project, I consider this job to be suitable to my area of knowledge and skills. I would love to work together to make this project a reality. I greatly appreciate the time provided and I remain pending for any questions or comments. Feel free to contact me. Greetings

@jonasdedzo

Hi there, I understand the real goal is to turn your mining drill data into operational decisions—not another historical dashboard. I can build a repeatable Python/SQL pipeline that cleans fleet CSVs, pressure/torque time-series and geology logs, aligns timestamps and produces reliable features for cycle-time, idle-time, throughput and maintenance analysis. From there, I’d combine descriptive analysis with predictive/prescriptive modelling to identify bottlenecks, abnormal operating patterns and maintenance signals. The results would feed a practical Power BI/Tableau dashboard showing the KPIs supervisors actually need, while the final report would translate the findings into at least three quantified optimization opportunities. I’ll make the workflow reproducible on fresh datasets, document the transformations and ensure dashboard outputs can refresh without manual rework. Where the data supports it, I’ll also segment performance by rig, site, shift, drilling conditions and geology to avoid misleading averages. If you share a representative sample of the drill and sensor data, I can quickly assess data quality, available variables and the strongest modelling opportunities before implementation. Looking forward to working with you. Thanks

@ankits0192

Hello, You need operational optimization, not another historical mining report. I can turn drill telemetry, fleet logs, sensor time-series, and geology data into a repeatable pipeline that identifies cycle-time losses, idle patterns, throughput constraints, and maintenance opportunities. I’ll clean and align the CSV/sensor/geology data, engineer operational features, build descriptive + predictive/prescriptive models, and create KPI dashboards in Power BI/Tableau. The final report will translate the findings into field-ready actions, targeting at least 3 measurable optimization opportunities backed by data. Workflow: data audit → cleaning/time alignment → feature engineering → modeling → KPI dashboard → recommendations → fresh-data validation and documentation. I won’t claim prior mining/drill-domain experience I don’t have; my focus is applying robust Python/SQL/data-science methods to your operational data. Questions: * What telemetry fields and sampling frequency are available? * Do you already use Power BI, Tableau, or another BI platform? * Which KPI matters most initially: cycle time, utilization, throughput, fuel/energy, or downtime? Best regards, Ankit

@caajoys

Thank you for considering my proposal. I have gone through the requirements in detail. I can turn your mining operational datasets into repeatable, decision-focused insights that identify bottlenecks, reduce idle time, improve cycle efficiency and support proactive maintenance planning. I’ll begin by cleaning and structuring the CSV fleet data, pressure/torque time-series and geology logs, with clear documentation of assumptions and data-quality checks. I can then develop descriptive analysis followed by appropriate predictive/prescriptive modelling to identify measurable opportunities for throughput improvement and cost reduction. I’ll deliver a documented Python/R/SQL pipeline or notebook that can run end-to-end on fresh data, along with a Power BI/Tableau dashboard covering key operational KPIs, cycle times, idle hours, equipment performance, bottlenecks and actionable recommendations. A concise management report will translate the findings into practical actions, including at least three data-backed optimization opportunities where supported by the data. While I won’t claim specialized mining telemetry experience without reviewing your data, I have strong experience in data analysis, financial modelling, process improvement, automation and Power BI reporting, with a focus on converting complex datasets into actionable business insights. I’m ready to review a sample dataset and discuss the optimization objectives.

@MuhammadUzair16

Hi, I am a software engineer with over 16 years of experience turning operational and sensor data into reliable analytics, predictive models, and practical decisions. I have worked with time-series telemetry, equipment performance data, maintenance indicators, and automated reporting workflows that closely match the needs of this project. I can clean and align your fleet CSVs, pressure and torque feeds, and geology logs into a documented, repeatable pipeline. I will then identify cycle-time losses, idle-hour patterns, bottlenecks, and early maintenance signals, presenting the results through an automatically refreshing dashboard and a concise report with at least three data-backed actions your field supervisors can apply immediately. I will also validate the full workflow against a fresh dataset and document assumptions, data-quality issues, and refresh steps. Before starting, I would like to understand the approximate data volume, sampling frequency, equipment identifiers shared across sources, and your preferred dashboard platform. I can also help define the most useful operational KPIs if they are not yet standardized. Please contact me to discuss details.

@engruhulajom

Dear Client, I’m an experienced data and full-stack developer with 10+ years of experience, specializing in Python, SQL, data pipelines, time-series analysis, predictive modeling, and operational dashboards. I understand you need raw mining fleet, sensor, and geology data transformed into repeatable analyses that identify cycle-time inefficiencies, idle hours, maintenance risks, throughput constraints, and concrete cost-saving opportunities. My Python, pandas, SQL, machine-learning, time-series, Power BI/Tableau, and automation experience enables me to build a reproducible pipeline from ingestion through modeling, dashboard refresh, and actionable reporting. I can structure the analysis around predictive and prescriptive insights rather than only historical reporting. I can deliver the documented pipeline, automated dashboards, and an operational report highlighting at least three data-backed optimization opportunities. Best regards, Md Ruhul Ajom

@ziedb4

Hello, Your acceptance criteria are the right ones — a pipeline that reruns on a fresh dataset, dashboards that refresh unattended, and three wins your supervisors can act on. That's how I'd structure the work: 1. Ingestion and cleaning: a Python pipeline (pandas/Polars) handling the fleet CSV exports, pressure/torque time-series, and geology logs — timestamp alignment, sensor gaps, outliers and unit inconsistencies, all parameterised so a new export runs without edits. 2. Descriptive baseline: cycle times, idle hours, utilisation and downtime broken down by rig, shift and site — this is what makes the optimisation numbers credible. 3. Predictive/prescriptive layer: identifying the drivers of long cycles and idle time, plus early-warning signals on sensor drift for maintenance scheduling before failure. 4. Dashboard + short report: KPIs, bottlenecks, and three quantified recommendations with the estimated gain of each. My background is high-frequency time-series work in algorithmic trading: noisy sensor-like feeds, gap handling, feature engineering and production pipelines in Python. I have not worked on mining telemetry specifically — you asked us to flag domain experience, so I'd rather be straight with you. What I'd need from you is a sample export and a short call on what the fields actually mean on site. Can you share a sample dataset so I can confirm scope and timeline before we commit?

@AndrewJSMAN

I’ve built ETL pipelines for drilling telemetry and fleet sensor data before, so this is standard work. I’ll ingest CSV sensor logs, geology tables, and fleet feeds into a Python pipeline using pandas and SQLAlchemy for cleaning and schema standardization. Time-series anomalies will be flagged with PyOD, cycle times optimized via survival analysis (lifelines), and maintenance windows scheduled with XGBoost. Dashboards will hit Tableau via Extract API, refreshing daily without manual input. Outputs will include a reproducible Jupyter notebook, a Tableau workbook with drill-cycle KPIs, and a 5-page ops memo with three prioritized optimization wins. Thanks, Andrii.