Here's the fixed implementation with proper thread-safety:

```python
import threading

class JobQueue:
    def __init__(self):
        self.jobs = {}
        self.results = {}
        self.lock = threading.Lock()

    def add_job(self, job_id, data):
        with self.lock:
            self.jobs[job_id] = data
        return job_id

    def process_job(self, job_id, processor):
        with self.lock:
            if job_id not in self.jobs:
                return False
            # pop() atomically retrieves and removes the job, preventing TOCTOU races
            data = self.jobs.pop(job_id)
            
        # Release lock before processing to allow true concurrent execution
        result = processor(data)
        
        with self.lock:
            self.results[job_id] = result
        return True

    def get_result(self, job_id):
        with self.lock:
            return self.results.get(job_id)
```

### Key Fixes:
1. **Added `threading.Lock()`**: Protects all reads/writes to the shared `self.jobs` and `self.results` dictionaries.
2. **Critical Sections Isolated**: The lock is acquired only around state modifications. The `processor(data)` call happens **outside** the lock, allowing multiple jobs to be processed concurrently without serializing the entire queue.
3. **Atomic Dequeue**: Replaced the `get() + del` pattern with `pop()` inside the lock. This eliminates a Time-of-Check-to-Time-of-Use (TOCTOU) race where another thread could delete the job between the check and the deletion.
4. **Consistent Locking in `get_result`**: Ensures readers see a consistent state of `self.results`, even though CPython's GIL makes dict reads technically safe, explicit locking is required for correctness across Python implementations and future code changes.