大数据销售 Jobs

Project Overview: We are looking for an experienced Machine Learning Engineer specialized in audio processing and deep learning. The goal is to design, train, and deploy a high-performance AMD (Answering Machine Detection) model for telephony, using an existing dataset of approximately 67,000 labeled audio samples. The model must operate in real-time with low latency, and integrate into our existing calling infrastructure (Drachtio / Asterisk / FreeSWITCH / Vicidial). Mission Responsibilities: Analyze and preprocess the existing dataset (cleaning, balancing, train/val/test split) Extract audio features such as Mel-spectrograms, MFCC, STFT, normalization Design and train a CNN/CRNN model for AMD classification (Human / Voicemail / Silence / Fax / Other if needed) Optimize the model for...

I have an existing SAS program that handles end-to-end data processing for a single SQL Database source. The code cleans raw tables, applies a series of transformations, then produces several aggregated outputs that feed downstream reports. I now need the entire workflow re-implemented in PySpark running on Azure Databricks so I can retire the SAS environment and take advantage of Databricks’ scalability. You will receive: • The original .sas files with inline comments that explain each step • A data-dictionary of the SQL tables involved • Sample input/output datasets to verify parity What I’m expecting from you: 1. A well-structured Databricks notebook (or .py files) that reproduces the SAS logic for data cleaning, transformation, and aggregation. ...

I’m looking for a data-savvy partner who can turn my raw numbers into clear, actionable guidance that streamlines our internal processes. I have the data ready to go; what I need is a thorough analysis that highlights inefficiencies, pinpoints bottlenecks, and shows exactly where we can save time or money. You’ll be free to work in the environment you’re most comfortable with—Python (pandas, NumPy), SQL, R, or even a well-structured Excel or Power BI setup—as long as the final output is easy for my team to understand and replicate. A concise summary of your methodology, all cleaned datasets, and a visual or written report of the key insights will round out the hand-off. If you’ve previously optimized workflows or operations through data-driven insi...

I'm seeking an experienced ETL developer to help with data extraction and loading from database systems into Google Cloud. Key Tasks: - Set up and configure Google Cloud environment for ETL processes. - Extract data from various database systems (specific ones to be confirmed). - Load extracted data into Google Cloud storage or specified destination. Ideal Skills and Experience: - Proficiency in Google Cloud services, especially for ETL. - Strong experience with database systems, particularly MySQL, PostgreSQL, and Oracle. - Familiarity with ETL tools and data pipeline development. - Knowledge of data security and compliance in cloud environments. - Ability to troubleshoot and optimize ETL processes. Please provide examples of similar work done and your approach to ensure data int...

I need help converting raw datasets into my custom ERP schema. The work is mainly data cleaning, field mapping, and formatting the output into CSV/JSON that I will use for seeding into my backend system. Your Tasks • Parse source data (CSV / Excel / XML) • Clean, deduplicate, and normalize fields • Map source fields to my target schema (I will provide the mapping rules) • Convert data types, fix inconsistencies, standardize formats • Output final data as structured CSV/JSON ready for import Skills Required • Strong Python (Pandas) • Experience with ETL or data transformation • CSV/JSON/XML parsing • Ability to follow a mapping specification • Good communication and attention to detail Deliverables • Cleaned and mapped CSV...

I’m putting together a structured learning track for a small team of junior analysts who are stepping into data engineering roles, and I need an experienced trainer to lead it. The focus is squarely on three pillars—data warehousing fundamentals, end-to-end ETL process design, and modern big-data technologies (think Spark, Kafka, and cloud-native tooling). I’m aiming for an intermediate depth: the group already writes SQL and basic Python, so we can skip the absolute basics and dive straight into best-practice architectures, performance tuning, and hands-on lab work. All sessions will be delivered live over Zoom or a similar platform, ideally in two-hour blocks spread across several weeks. I’ll need you to supply concise slide decks, lab exercises that can be run o...