Telemetry Data Scientist

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    "requisitionDescription": "<div><p id=\"isPasted\"><strong>Telemetry Data Scientist / Real-Time ML Engineer</strong></p><p><strong>Division:</strong> DATUM, Impac Exploration Services<br><strong>Location:</strong> Remote or Oklahoma City<br><strong>Type:</strong> Full-Time or Contract</p><p><strong>Build Models That Run at 10,000 Feet Per Second</strong></p><p>We&#39;re not looking for someone to build another dashboard. We&#39;re looking for someone who sees streaming sensor data and thinks &quot;I could predict that failure 30 seconds out.&quot;</p><p>DATUM is where industrial operations meet cutting-edge ML. We&#39;re streaming terabytes of WITS telemetry from drilling operations&mdash;pressure, torque, vibration, flow rates&mdash;all live from assets worth millions per day. This isn&#39;t historical analysis. This is real-time ML where your models make decisions while the drill bit is turning.</p><p>Here&#39;s what makes this interesting: The physics are unforgiving. The data is messy. The stakes are real. And when your anomaly detection prevents a catastrophic failure at 10,000 feet? That&#39;s the kind of win that gets noticed&mdash;by operators, by industry, and yeah, by recruiters from places you&#39;ve dreamed about working.</p><p><strong>What You&#39;ll Actually Build</strong></p><ul><li>Streaming ML pipelines that process millions of sensor readings per minute</li><li>Anomaly detection that understands the difference between &quot;weird but fine&quot; and &quot;stop everything now&quot;</li><li>Predictive models that see patterns in vibration harmonics humans miss</li><li>Real-time feature engineering on streams that never stop</li><li>Decision systems that earn trust from people who&#39;ve been reading these signals since before you were born</li></ul><p><strong>Technical Reality Check</strong></p><p>Core stack:</p><ul><li><strong>Streaming:</strong> Kafka/Pulsar, Flink/Spark Streaming, or River (bonus if you&#39;ve pushed these to their limits)</li><li><strong>ML Ops:</strong> MLflow, Weights &amp; Biases, or Kubeflow&mdash;because models in notebooks don&#39;t save rigs</li><li><strong>Time Series:</strong> Prophet, LSTM/GRU implementations, or custom architectures for sequential data</li><li><strong>Languages:</strong> Python (NumPy, Pandas, Scikit-learn) + SQL + whatever else gets the job done</li><li><strong>Deployment:</strong> Docker, Kubernetes, edge computing frameworks</li><li><strong>Monitoring:</strong> Grafana, Prometheus, or custom solutions&mdash;because you can&#39;t fix what you can&#39;t see</li></ul><p>Nice to have:</p><ul><li>Physics-informed neural networks or domain-aware ML</li><li>Experience with industrial protocols (OPC-UA, MQTT, Modbus)</li><li>Edge computing / model optimization for embedded systems</li><li>That one project where you made TensorFlow run somewhere it shouldn&#39;t</li></ul><p><strong>You&#39;re Our Person If</strong></p><ul><li>You&#39;ve built something real-time that stayed up when it mattered</li><li>Sensor fusion excites you more than computer vision</li><li>You can explain Kalman filters and gradient boosting to a drilling engineer</li><li>You&#39;ve debugged model drift at 3 AM because production doesn&#39;t sleep</li><li>&quot;It works in my notebook&quot; isn&#39;t in your vocabulary</li></ul><p><strong>Even Better If</strong></p><ul><li>You&#39;ve touched industrial systems, IoT, or anything with real sensors</li><li>Built telemetry systems for fun (racing sims, drone projects, even elaborate Raspberry Pi setups)</li><li>Contributed to time-series or streaming ML libraries</li><li>You understand that 99.9% uptime means 8.76 hours of downtime per year&mdash;and that&#39;s too much</li></ul><p><strong>The Culture</strong></p><p>Small team. Big problems. No bureaucracy. We move fast, ship faster, and celebrate when our work is so good that other companies try to poach you. You&#39;ll have the compute you need, the freedom to experiment, and colleagues who push you to be better.</p><p>Fair warning: This isn&#39;t a &quot;set it and forget it&quot; role. When a $100M operation depends on your model, you&#39;ll feel it. But when your algorithm catches something nobody else saw? That feeling is addictive.</p><p><strong>Why This Matters</strong></p><p>While everyone else fights over chatbot improvements, we&#39;re building ML for the physical world. Energy is complex, consequential, and desperately needs fresh thinking. Come help us prove that the best telemetry systems don&#39;t just live at race tracks&mdash;they&#39;re 10,000 feet underground, running 24/7, keeping people safe and operations efficient.</p><p>Ready to build ML that matters? Let&#39;s talk.</p><p><br></p><p><strong>We are not sponsoring visas or participating in CPT programs at this time.</strong></p></div>\n",
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