This race condition occurs because multiple threads can simultaneously read and write to `self.jobs` and `self.results` without coordination. Specifically, when one thread is in the middle of `process_job`, another thread could modify the dictionaries, leading to data corruption, lost updates, or inconsistent state.

The fix involves using a `threading.Lock` to ensure that only one thread can access the shared state (the dictionaries) at any given time.

### Fixed Implementation

```python
import threading

class JobQueue:
    def __init__(self):
        self.jobs = {}
        self.results = {}
        # Initialize the lock to protect shared resources (self.jobs and self.results)
        self._lock = threading.Lock()

    def add_job(self, job_id, data):
        """Adds a job to the queue, protected by the lock."""
        with self._lock:
            self.jobs[job_id] = data
        return job_id

    def process_job(self, job_id, processor):
        """
        Processes a job. The lock is used to atomically retrieve and remove the job,
        but the potentially slow processor runs outside the lock to prevent blocking
        other threads. The lock is reacquired to safely write the result.
        """
        data = None
        
        # 1. Acquire lock to safely check existence and remove the job
        with self._lock:
            if job_id not in self.jobs:
                return False
            # Use .pop() to atomically retrieve and remove the job
            data = self.jobs.pop(job_id)
        
        # 2. Run the processor outside the lock (Computation phase)
        # This prevents the lock from holding up the entire system while the job runs.
        result = processor(data)

        # 3. Acquire lock again to safely write the result
        with self._lock:
            self.results[job_id] = result
            return True

    def get_result(self, job_id):
        """Retrieves a result, protected by the lock."""
        with self._lock:
            return self.results.get(job_id)

# Example Usage (Demonstration of thread safety)
if __name__ == '__main__':
    queue = JobQueue()
    
    def job_processor(data):
        import time
        time.sleep(0.01) # Simulate work
        return f"Processed_{data}"

    # Add jobs
    job1_id = queue.add_job("job1", "data1")
    job2_id = queue.add_job("job2", "data2")
    
    # Simulate multiple threads processing jobs concurrently
    def worker(job_id, processor):
        success = queue.process_job(job_id, processor)
        print(f"Worker attempt on {job_id}: {'Success' if success else 'Failed (Job not found)'}")

    threads = []
    threads.append(threading.Thread(target=worker, args=(job1_id, job_processor)))
    threads.append(threading.Thread(target=worker, args=(job2_id, job_processor)))
    # Attempt to process a non-existent job simultaneously
    threads.append(threading.Thread(target=worker, args=("job3", job_processor))) 

    for t in threads:
        t.start()

    for t in threads:
        t.join()

    print("\n--- Final Results ---")
    print(f"Result 1: {queue.get_result(job1_id)}")
    print(f"Result 2: {queue.get_result(job2_id)}")
    print(f"Result 3 (missing): {queue.get_result('job3')}")
```

### Explanation of the Fixes

1.  **`self._lock = threading.Lock()`**: A lock object is initialized in the constructor. This object acts as a gatekeeper.
2.  **`with self._lock:`**: This context manager ensures that when a thread enters the block, it acquires the lock. If another thread already holds the lock, the current thread pauses until the lock is released. When the block exits (even if an error occurs), the lock is automatically released.
3.  **Atomic Operations in `process_job`**:
    *   The most critical change was in `process_job`. We now use `self.jobs.pop(job_id)` *inside* the lock. This guarantees that the check (`if job_id in self.jobs`) and the removal (`del self.jobs[job_id]`) happen as a single, atomic operation. No other thread can interfere between checking for the job and removing it.
    *   We moved the potentially long-running `processor(data)` call **outside** the lock. If we held the lock during computation, the entire queue would freeze while one job ran. By releasing the lock during computation, other threads can continue to add or process jobs.
    *   The lock is reacquired only for the final, quick write to `self.results`.
4.  **Protection of Reads/Writes**: `add_job` and `get_result` are also wrapped in the lock to ensure thread-safe access to the dictionaries.